@inproceedings{10.5555/1619332.1619409,
author = {Sang, Tian and Beame, Paul and Kautz, Henry},
title = {Performing Bayesian Inference by Weighted Model Counting},
year = {2005},
isbn = {157735236x},
publisher = {AAAI Press},
abstract = {Over the past decade general satisfiability testing algorithms have proven to be surprisingly effective at solving a wide variety of constraint satisfaction problem, such as planning and scheduling (Kautz and Selman 2003). Solving such NP-complete tasks by "compilation to SAT" has turned out to be an approach that is of both practical and theoretical interest. Recently, (Sang et al. 2004) have shown that state of the art SAT algorithms can be efficiently extended to the harder task of counting the number of models (satisfying assignments) of a formula, by employing a technique called component caching. This paper begins to investigate the question of whether "compilation to model-counting" could be a practical technique for solving real-world #P-complete problems, in particular Bayesian inference. We describe an efficient translation from Bayesian networks to weighted model counting, extend the best model-counting algorithms to weighted model counting, develop an efficient method for computing all marginals in a single counting pass, and evaluate the approach on computationally challenging reasoning problems.},
booktitle = {Proceedings of the 20th National Conference on Artificial Intelligence - Volume 1},
pages = {475–481},
numpages = {7},
location = {Pittsburgh, Pennsylvania},
series = {AAAI'05}
}@article{DBLP:journals/corr/abs-2106-04953,
  author    = {Tim Reichelt and
               Adam Golinski and
               Luke Ong and
               Tom Rainforth},
  title     = {Expectation Programming},
  journal   = {CoRR},
  volume    = {abs/2106.04953},
  year      = {2021},
  hideurl       = {https://arxiv.org/abs/2106.04953},
  eprinttype = {arXiv},
  eprint    = {2106.04953},
  timestamp = {Tue, 15 Jun 2021 16:35:15 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-04953.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{langley00,
 author    = {P. Langley},
 title     = {Crafting Papers on Machine Learning},
 year      = {2000},
 pages     = {1207--1216},
 editor    = {Pat Langley},
 booktitle     = {Proceedings of the 17th International Conference
              on Machine Learning (ICML 2000)},
 address   = {Stanford, CA},
 publisher = {Morgan Kaufmann}
}

@TechReport{mitchell80,
  author = 	 "T. M. Mitchell",
  title = 	 "The Need for Biases in Learning Generalizations",
  institution =  "Computer Science Department, Rutgers University",
  year = 	 "1980",
  address =	 "New Brunswick, MA",
}

@phdthesis{kearns89,
  author = {M. J. Kearns},
  title =  {Computational Complexity of Machine Learning},
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  year =   {1989}
}

@Book{MachineLearningI,
  editor = 	 "R. S. Michalski and J. G. Carbonell and T.
		  M. Mitchell",
  title = 	 "Machine Learning: An Artificial Intelligence
		  Approach, Vol. I",
  publisher = 	 "Tioga",
  year = 	 "1983",
  address =	 "Palo Alto, CA"
}

@Book{DudaHart2nd,
  author =       "R. O. Duda and P. E. Hart and D. G. Stork",
  title =        "Pattern Classification",
  publisher =    "John Wiley and Sons",
  edition =      "2nd",
  year =         "2000"
}

@misc{anonymous,
  title= {Suppressed for Anonymity},
  author= {Author, N. N.},
  year= {2021}
}

@InCollection{Newell81,
  author =       "A. Newell and P. S. Rosenbloom",
  title =        "Mechanisms of Skill Acquisition and the Law of
                  Practice", 
  booktitle =    "Cognitive Skills and Their Acquisition",
  pages =        "1--51",
  publisher =    "Lawrence Erlbaum Associates, Inc.",
  year =         "1981",
  editor =       "J. R. Anderson",
  chapter =      "1",
  address =      "Hillsdale, NJ"
}


@Article{Samuel59,
  author = 	 "A. L. Samuel",
  title = 	 "Some Studies in Machine Learning Using the Game of
		  Checkers",
  journal =	 "IBM Journal of Research and Development",
  year =	 "1959",
  volume =	 "3",
  number =	 "3",
  pages =	 "211--229"
}

@misc{luhn,
 title={Computer for Verifying Numbers},
 author={H.P. Luhn},
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 year = {1960}
 }

@book{darwiche_2009, place={Cambridge}, title={Modeling and Reasoning with Bayesian Networks}, DOI={10.1017/CBO9780511811357}, publisher={Cambridge University Press}, author={Darwiche, Adnan}, year={2009}}


@misc{dippl,
  title = {{The Design and Implementation of Probabilistic Programming Languages}},
  author = {Goodman, Noah D and Stuhlm\"{u}ller, Andreas},
  year = {2014},
  howpublished = {\url{http://dippl.org}},
  note = {Accessed: 2022-10-26}
}

@article{tolpin2016design,
  title = {Design and Implementation of Probabilistic Programming Language Anglican},
  author = {Tolpin, David and van de Meent, Jan Willem and Yang, Hongseok and Wood, Frank},
  journal = {arXiv preprint arXiv:1608.05263},
  year = {2016}
}

@article{JSSv076i01,
 title={Stan: A Probabilistic Programming Language},
 volume={76},
 hideurl       ={https://www.jstatsoft.org/index.php/jss/article/view/v076i01},
 doi={10.18637/jss.v076.i01},
 abstract={Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.},
 number={1},
 journal={Journal of Statistical Software},
 author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
 year={2017},
 pages={1–32}
}

@inproceedings{gehr2016psi, title={Psi: Exact symbolic inference for probabilistic programs}, author={Gehr, Timon and Misailovic, Sasa and Vechev, Martin}, booktitle={International Conference on Computer Aided Verification}, pages={62--83}, year={2016}, organization={Springer}}

@proceedings{Biere2009,
  added-at = {2011-12-03T17:47:36.000+0100},
  biburl = {https://www.bibsonomy.org/bibtex/2d9fd232df370a1c69cbab8e955bc900f/henningb},
  booktitle = {Handbook of Satisfiability},
  description = {dblp},
  editor = {Biere, Armin and Heule, Marijn and van Maaren, Hans and Walsh, Toby},
  groups = {public},
  interhash = {bc1777bb3606368d9089d58ab84318c3},
  intrahash = {d9fd232df370a1c69cbab8e955bc900f},
  isbn = {978-1-58603-929-5},
  keywords = {smt},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  timestamp = {2012-02-24T16:46:23.000+0100},
  title = {Handbook of Satisfiability},
  url = {http://dblp.uni-trier.de/db/series/faia/faia185.html},
  username = {henningb},
  volume = 185,
  year = 2009
}



@inproceedings{DBLP:conf/vmcai/ZoharIMNNPRBT22,
  author    = {Yoni Zohar and
               Ahmed Irfan and
               Makai Mann and
               Aina Niemetz and
               Andres N{\"{o}}tzli and
               Mathias Preiner and
               Andrew Reynolds and
               Clark W. Barrett and
               Cesare Tinelli},
  editor    = {Bernd Finkbeiner and
               Thomas Wies},
  title     = {Bit-Precise Reasoning via Int-Blasting},
  booktitle = {Verification, Model Checking, and Abstract Interpretation - 23rd International
               Conference, {VMCAI} 2022, Philadelphia, PA, USA, January 16-18, 2022,
               Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {13182},
  pages     = {496--518},
  publisher = {Springer},
  year      = {2022},
  hideurl       = {https://doi.org/10.1007/978-3-030-94583-1\_24},
  doi       = {10.1007/978-3-030-94583-1\_24},
  timestamp = {Fri, 21 Jan 2022 22:02:46 +0100},
  biburl    = {https://dblp.org/rec/conf/vmcai/ZoharIMNNPRBT22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{huang2021aqua,
  title={AQUA: Automated Quantized Inference for Probabilistic Programs},
  author={Huang, Zixin and Dutta, Saikat and Misailovic, Sasa},
  booktitle={International Symposium on Automated Technology for Verification and Analysis},
  pages={229--246},
  year={2021},
  organization={Springer}
}

@misc{https://doi.org/10.48550/arxiv.1806.02027,
  doi = {10.48550/ARXIV.1806.02027},
  
  url = {https://arxiv.org/abs/1806.02027},
  
  author = {Wu, Yi and Srivastava, Siddharth and Hay, Nicholas and Du, Simon and Russell, Stuart},
  
  keywords = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Programming Languages (cs.PL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@article{bingham2018pyro,
author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and
          Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and
          Horsfall, Paul and Goodman, Noah D.},
title = {{Pyro: Deep Universal Probabilistic Programming}},
journal = {Journal of Machine Learning Research},
year = {2018}
}

@misc{https://doi.org/10.48550/arxiv.1111.4246,
  doi = {10.48550/ARXIV.1111.4246},
  
  url = {https://arxiv.org/abs/1111.4246},
  
  author = {Hoffman, Matthew D. and Gelman, Andrew},
  
  keywords = {Computation (stat.CO), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
  
  publisher = {arXiv},
  
  year = {2011},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{article,
author = {Moradi, Eghbal and Ghezel Arsalan, Mohammad Reza and Naimi Sadigh, Ali and Ghalb, Hamed},
year = {2011},
month = {01},
pages = {285-312},
title = {Quantitative Models in Supply Chain Management},
isbn = {9781613505052},
journal = {Supply Chain Sustainability and Raw Material Management: Concepts and Processes},
doi = {10.4018/978-1-61350-504-5.ch016}
}

@article{https://doi.org/10.48550/arxiv.1812.07259,
  doi = {10.48550/ARXIV.1812.07259},
  
  url = {https://arxiv.org/abs/1812.07259},
  
  author = {Malsiner-Walli, Gertraud and Wagner, Helga},
  
  keywords = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Comparing Spike and Slab Priors for Bayesian Variable Selection},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{10.1145/3290352,
author = {Smith, Calvin and Hsu, Justin and Albarghouthi, Aws},
title = {Trace Abstraction modulo Probability},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {3},
number = {POPL},
url = {https://doi.org/10.1145/3290352},
doi = {10.1145/3290352},
abstract = {We propose trace abstraction modulo probability, a proof technique for verifying high-probability accuracy guarantees of probabilistic programs. Our proofs overapproximate the set of program traces using failure automata, finite-state automata that upper bound the probability of failing to satisfy a target specification. We automate proof construction by reducing probabilistic reasoning to logical reasoning: we use program synthesis methods to select axioms for sampling instructions, and then apply Craig interpolation to prove that traces fail the target specification with only a small probability. Our method handles programs with unknown inputs, parameterized distributions, infinite state spaces, and parameterized specifications. We evaluate our technique on a range of randomized algorithms drawn from the differential privacy literature and beyond. To our knowledge, our approach is the first to automatically establish accuracy properties of these algorithms.},
journal = {Proc. ACM Program. Lang.},
month = {jan},
articleno = {39},
numpages = {31},
keywords = {Craig interpolation, union bound, probabilistic program verification, Trace abstraction}
}

@inproceedings{10.1145/2491411.2491423,
author = {Claret, Guillaume and Rajamani, Sriram K. and Nori, Aditya V. and Gordon, Andrew D. and Borgstr\"{o}m, Johannes},
title = {Bayesian Inference Using Data Flow Analysis},
year = {2013},
isbn = {9781450322379},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2491411.2491423},
doi = {10.1145/2491411.2491423},
abstract = {We present a new algorithm for Bayesian inference over probabilistic programs, based on data flow analysis techniques from the program analysis community. Unlike existing techniques for Bayesian inference on probabilistic programs, our data flow analysis algorithm is able to perform inference directly on probabilistic programs with loops. Even for loop-free programs, we show that data flow analysis offers better precision and better performance benefits over existing techniques. We also describe heuristics that are crucial for our inference to scale, and present an empirical evaluation of our algorithm over a range of benchmarks.},
booktitle = {Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering},
pages = {92–102},
numpages = {11},
keywords = {probabilistic programming, data flow analysis, algebraic decision diagrams},
location = {Saint Petersburg, Russia},
series = {ESEC/FSE 2013}
}

@article{Piironen_2017,
	doi = {10.1214/17-ejs1337si},
  
	url = {https://doi.org/10.1214%2F17-ejs1337si},
  
	year = 2017,
	month = {jan},
  
	publisher = {Institute of Mathematical Statistics},
  
	volume = {11},
  
	number = {2},
  
	author = {Juho Piironen and Aki Vehtari},
  
	title = {Sparsity information and regularization in the horseshoe and other shrinkage priors},
  
	journal = {Electronic Journal of Statistics}
}

@inproceedings{saad2021sppl,
title           = {{SPPL:} Probabilistic Programming with Fast Exact Symbolic Inference},
author          = {Saad, Feras A. and Rinard, Martin C. and Mansinghka, Vikash K.},
booktitle       = {PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Design and Implementation},
pages           = {804--819},
year            = 2021,
location        = {Virtual, Canada},
publisher       = {ACM},
address         = {New York, NY, USA},
doi             = {10.1145/3453483.3454078},
keywords        = {probabilistic programming, symbolic execution, static analysis},
}

@article{10.1145/1232420.1232423,
author = {Xie, Yichen and Aiken, Alex},
title = {Saturn: A Scalable Framework for Error Detection Using Boolean Satisfiability},
year = {2007},
issue_date = {May 2007},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {29},
number = {3},
issn = {0164-0925},
url = {https://doi.org/10.1145/1232420.1232423},
doi = {10.1145/1232420.1232423},
abstract = {This article presents Saturn, a general framework for building precise and scalable static error detection systems. Saturn exploits recent advances in Boolean satisfiability (SAT) solvers and is path sensitive, precise down to the bit level, and models pointers and heap data. Our approach is also highly scalable, which we achieve using two techniques. First, for each program function, several optimizations compress the size of the Boolean formulas that model the control flow and data flow and the heap locations accessed by a function. Second, summaries in the spirit of type signatures are computed for each function, allowing interprocedural analysis without a dramatic increase in the size of the Boolean constraints to be solved.We have experimentally validated our approach by conducting two case studies involving a Linux lock checker and a memory leak checker. Results from the experiments show that our system scales well, parallelizes well, and finds more errors with fewer false positives than previous static error detection systems.},
journal = {ACM Trans. Program. Lang. Syst.},
month = {may},
pages = {16–es},
numpages = {43},
keywords = {Program analysis, error detection, Boolean satisfiability}
}

@article{Bissiri_2016,
	doi = {10.1111/rssb.12158},
  
	url = {https://doi.org/10.1111%2Frssb.12158},
  
	year = 2016,
	month = {feb},
  
	publisher = {Wiley},
  
	volume = {78},
  
	number = {5},
  
	pages = {1103--1130},
  
	author = {P. G. Bissiri and C. C. Holmes and S. G. Walker},
  
	title = {A general framework for updating belief distributions},
  
	journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}
}

@misc{conjugate_gaussians,
    hideurl       ={http://www.cs.berkeley.edu/~jordan/courses/260-spring10/lectures/lecture5.pdf},
    title={Stat260: Bayesian Modeling and Inference: The Conjugate Prior for the Normal Distribution},
    first={Michael I.},
    last={Jordan},
    date={February 8, 2010}}
    
@misc{dice_github,
    hideurl       ={https://github.com/SHoltzen/dice},
    title={Dice Github repository},
    first={Steven},
    last={Holtzen},
    date={April 22, 2022}}

@misc{https://doi.org/10.48550/arxiv.1312.5386,
  doi = {10.48550/ARXIV.1312.5386},
  
  url = {https://arxiv.org/abs/1312.5386},
  
  author = {Nishihara, Robert and Minka, Thomas and Tarlow, Daniel},
  
  keywords = {Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Detecting Parameter Symmetries in Probabilistic Models},
  
  publisher = {arXiv},
  
  year = {2013},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{spacex,
    author={canyon289},
    title={Spacex},
	url = {https://gist.github.com/canyon289/73890bab211c5cbaea41ad6f32df01a5},
    year = {2022},
	author = {https://gist.github.com/canyon289},
}

@misc{spike_and_slab,
    author={Wessel Bruinsma},
    title={spike and slab},
	url = {https://wesselb.github.io/assets/write-ups/Bruinsma,%20Spike%20and%20Slab%20Priors.pdf},
}

@article{Julia-2017,
    title={Julia: A fresh approach to numerical computing},
    author={Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B},
    journal={SIAM {R}eview},
    volume={59},
    number={1},
    pages={65--98},
    year={2017},
    publisher={SIAM},
    doi={10.1137/141000671},
    hideurl       ={https://epubs.siam.org/doi/10.1137/141000671}
}

@inproceedings{DBLP:conf/pldi/FlanaganSDF93,
  author    = {Cormac Flanagan and
               Amr Sabry and
               Bruce F. Duba and
               Matthias Felleisen},
  editor    = {Robert Cartwright},
  title     = {The Essence of Compiling with Continuations},
  booktitle = {Proceedings of the {ACM} SIGPLAN'93 Conference on Programming Language
               Design and Implementation (PLDI), Albuquerque, New Mexico, USA, June
               23-25, 1993},
  pages     = {237--247},
  publisher = {{ACM}},
  year      = {1993},
  hideurl       = {https://doi.org/10.1145/155090.155113},
  doi       = {10.1145/155090.155113},
  timestamp = {Tue, 06 Nov 2018 16:59:30 +0100},
  biburl    = {https://dblp.org/rec/conf/pldi/FlanaganSDF93.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InBook{sharir:81,
  author =  "M. Sharir and Amir Pnueli",
  title = "Two approaches to interprocedural data flow analysis",
  booktitle = "Program Flow Analysis: Theory and Applications",
  editor =  "S. Muchnick and N. Jones",
  publisher = "Prentice-Hall",
  address = "Englewood Cliffs, NJ",
  year =  "1981",
}

@article{lew2019trace,
  title={Trace types and denotational semantics for sound programmable inference in probabilistic languages},
  author={Lew, Alexander K and Cusumano-Towner, Marco F and Sherman, Benjamin and Carbin, Michael and Mansinghka, Vikash K},
  journal={Proceedings of the ACM on Programming Languages},
  volume={4},
  number={POPL},
  pages={1--32},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{Claret2013,
author = {Claret, Guillaume and Rajamani, Sriram K. and Nori, Aditya V. and Gordon, Andrew D. and Borgstr{\"{o}}m, Johannes},
doi = {10.1145/2491411.2491423},
fil = {::},
isbn = {9781450322379},
journal = {Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2013},
keywords = {algebraic decision diagrams,probabilistic programming},
pages = {92},
title = {{Bayesian inference using data flow analysis}},
url = {http://dl.acm.org/citation.cfm?doid=2491411.2491423},
year = {2013}
}

@article{Sampson2014,
author = {Sampson, Adrian and Panchekha, Pavel and Mytkowicz, Todd and McKinley, Kathryn S and Grossman, Dan and Ceze, Luis},
doi = {10.1145/2594291.2594294},
file = {:Users/sholtzen/Library/Application Support/Mendeley Desktop/Downloaded/Sampson et al. - 2014 - Expressing and Verifying Probabilistic Assertions.pdf:pdf},
isbn = {978-1-4503-2784-8},
issn = {15232867},
journal = {Proceedings of the 35th Conference on Programming Language Design and Implementation (PLDI)},
keywords = {approximate computing,data obfuscation,differential privacy,probabilistic programming,sensors,symbolic execution},
pages = {112--122},
title = {{Expressing and Verifying Probabilistic Assertions}},
url = {http://doi.acm.org/10.1145/2594291.2594294},
year = {2014}
}

@InProceedings{Ball2001,
author = {Ball, Thomas and Majumdar, Rupak and Millstein, Todd and Rajamani,
                  Sriram K.},
title = {Automatic predicate abstraction of C programs},
booktitle = {Proc.~of PLDI},
year = {2001},
pages = {203--213},
numpages = {11},

}

@Book{dijkstra76,
  author =  "Edsger W. Dijkstra",
  title =   "A Discipline of Programming",
  publisher =     "Prentice-Hall",
  year =    "1976",
  address = "Englewood Cliffs, New Jersey",
 }

@InProceedings{GrafSaidi97,
   author =  "Susanne Graf and Hassen Sa{\"\i{}}di",
  booktitle =  "Proc.~of CAV",
  month =   jun,
  pages =   "72--83",
  publisher = "Springer-Verlag",
  title =   "Construction of Abstract State Graphs with {PVS}",
  volume =  "1254",
  year =    "1997",
}

@article{Monniaux2001,
 author = {Monniaux, David},
 title = {An Abstract Monte-Carlo Method for the Analysis of Probabilistic
                  Programs},
 journal = {SIGPLAN Not.},
 issue_date = {March 2001},
 volume = {36},
 number = {3},
 month = jan,
 year = {2001},
 issn = {0362-1340},
 pages = {93--101},
 numpages = {9},
 doi = {10.1145/373243.360211},
 acmid = {360211},
 publisher = {ACM},
 address = {New York, NY, USA},
}

@article{Sankaranarayanan2013,
 author = {Sankaranarayanan, Sriram and Chakarov, Aleksandar and Gulwani,
                  Sumit},
 title = {Static Analysis for Probabilistic Programs: Inferring Whole Program
                  Properties from Finitely Many Paths},
 journal = {SIGPLAN Not.},
 issue_date = {June 2013},
 volume = {48},
 number = {6},
 month = jun,
 year = {2013},
 issn = {0362-1340},
 pages = {447--458},
 numpages = {12},
 doi = {10.1145/2499370.2462179},
 acmid = {2462179},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {monte-carlo sampling, probabilistic programming, program
                  verification, symbolic execution, volume bounding},
}

@inproceedings{Sampson2014,
 author = {Sampson, Adrian and Panchekha, Pavel and Mytkowicz, Todd and
                  McKinley, Kathryn S. and Grossman, Dan and Ceze, Luis},
 title = {Expressing and Verifying Probabilistic Assertions},
 booktitle = {Proceedings of the 35th ACM SIGPLAN Conference on Programming
                  Language Design and Implementation},
 series = {PLDI},
 year = {2014},
 isbn = {978-1-4503-2784-8},
 location = {Edinburgh, United Kingdom},
 pages = {112--122},
 numpages = {11},
 acmid = {2594294},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {approximate computing, data obfuscation, differential privacy,
                  probabilistic programming, sensors, symbolic execution},
}


@inproceedings{Chistikov2015,
 author = {Chistikov, Dmitry and Dimitrova, Rayna and Majumdar, Rupak},
 title = {Approximate Counting in SMT and Value Estimation for Probabilistic Programs},
 booktitle = {Proc.~of TACAS},
 year = {2015},
 isbn = {978-3-662-46680-3},
 pages = {320--334},
 numpages = {15},
 myurl = {http://dx.doi.org/10.1007/978-3-662-46681-0_26},
 doi = {10.1007/978-3-662-46681-0_26},
 acmid = {2945619},
 publisher = {Springer-Verlag New York, Inc.},
 address = {New York, NY, USA},
} 

@inproceedings{10.5555/3061053.3061128,
author = {Wu, Yi and Li, Lei and Russell, Stuart and Bodik, Rastislav},
title = {Swift: Compiled Inference for Probabilistic Programming Languages},
year = {2016},
isbn = {9781577357704},
publisher = {AAAI Press},
abstract = {A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines--even the compiled ones--incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence},
pages = {3637–3645},
numpages = {9},
location = {New York, New York, USA},
series = {IJCAI'16}
}




@article{Russell2015,
author = {Russell, Stuart J},
title = {{SWIFT : Compiled Inference for Probabilistic Programs}},
year = {2015}
}

@article{Grigore2016,
abstract = {The core challenge in designing an effective static program analysis
                  is to find a good program abstraction -- one that retains only
                  details relevant to a given query. In this paper, we present a
                  new approach for automatically finding such an abstraction.
                  Our approach uses a pessimistic strategy, which can optionally
                  use guidance from a probabilistic model. Our approach applies
                  to parametric static analyses implemented in Datalog, and is
                  based on counterexample-guided abstraction refinement. For
                  each untried abstraction, our probabilistic model provides a
                  probability of success, while the size of the abstraction
                  provides an estimate of its cost in terms of analysis time.
                  Combining these two metrics, probability and cost, our
                  refinement algorithm picks an optimal abstraction. Our
                  probabilistic model is a variant of the Erdos-Renyi random
                  graph model, and it is tunable by what we call
                  hyperparameters. We present a method to learn good values for
                  these hyperparameters, by observing past runs of the analysis
                  on an existing codebase. We evaluate our approach on an object
                  sensitive pointer analysis for Java programs, with two client
                  analyses (PolySite and Downcast).},
archivePrefix = {arXiv},
arxivId = {1511.01874},
author = {Grigore, Radu and Yang, Hongseok},
doi = {10.1145/2837614.2837663},
eprint = {1511.01874},
isbn = {978-1-4503-3549-2},
issn = {15232867},
journal = {Proc.~of POPL},
keywords = {Datalog,Horn,hypergraph,probability},
pages = {485--498},
title = {{Abstraction Refinement Guided by a Learnt Probabilistic Model}},
year = {2016}
}

@InProceedings{Cousot2012,
author = {Cousot, Patrick and Monerau, Michael},
doi = {10.1007/978-3-642-28869-2\_9},
isbn = {9783642288685},
issn = {03029743},
booktitle = {Proc.~of ESOP},
pages = {169--193},
title = {{Probabilistic abstract interpretation}},
year = {2012}
}

@article{Pierro,
author = {Pierro, Alessandra Di and Wiklicky, Herbert},
number = {i},
title = {{Probabilistic Abstract Intepretation : From Trace Semantics to DTMCs
                  via Linear Regression}}
}

@article{Prekopa2003,
abstract = {Probabilistic programming means two strongly connected models as
                  well as the study of their mathematical properties, solutions
                  of the relevant optimization problems and their applications.
                  The two models are: maximizing (or minimizing) a probability
                  under constraints and programming under probabilistic
                  constraints. There are a number of variants and special cases
                  of these models and we present them in Section 1. In Section 2
                  we summarize those mathematical theories which can be used to
                  prove the convexity of large classes of our problems and we
                  also show how they can be applied in this context. In Section
                  3 we present solution algorithms of our stochastic programming
                  problems. Since we are handling probabilities of sets in
                  higher dimensional spaces, it is necessary to use bounding and
                  other approximation algorithms to find these probabilities
                  with satisfactory precision. This is the subject of Section 5.
                  In Section 4 we present two-stage and multi-stage problems
                  which are combined with probabilistic constraints. Some
                  duality and stability theorems are presented in Section 6.
                  Finally, in Section 7, we present applications of our model
                  constructions. {\textcopyright} 2003 Elsevier Inc. All rights
                  reserved.},
archivePrefix = {arXiv},
arxivId = {1312.4328},
author = {Pr{\'{e}}kopa, Andr{\'{a}}s},
doi = {10.1016/S0927-0507(03)10005-9},
eprint = {1312.4328},
isbn = {9781450328654},
issn = {09270507},
journal = {Handbooks in Operations Research and Management Science},
number = {C},
pages = {267--351},
title = {{Probabilistic Programming}},
volume = {10},
year = {2003}
}

@article{Chaganty2013,
author = {Chaganty, Arun and Nori, Aditya V and Rajamani, Sriram K},
issn = {15337928},
journal = {Proc.~of AISTATS},
pages = {153--160},
title = {{Efficiently Sampling Probabilistic Programs via Program Analysis}},
volume = {31},
year = {2013}
}

@inproceedings{Smolka_2019,
	doi = {10.1145/3314221.3314639},
  
	url = {https://doi.org/10.1145%2F3314221.3314639},
  
	year = 2019,
	month = {jun},
  
	publisher = {{ACM}
},
  
	author = {Steffen Smolka and Praveen Kumar and David M. Kahn and Nate Foster and Justin Hsu and Dexter Kozen and Alexandra Silva},
  
	title = {Scalable verification of probabilistic networks},
  
	booktitle = {Proceedings of the 40th {ACM} {SIGPLAN} Conference on Programming Language Design and Implementation}
}

@article{Hur2014,
abstract = {Probabilistic programs use familiar notation of programming
                  languages to specify probabilistic models. Suppose we are
                  interested in estimating the distribution or expectation of a
                  return expression r of a probabilistic program P. We are
                  interested in slicing the probabilistic program P and obtain a
                  simpler program SLI(P) which retains only those parts of P
                  that are relevant to estimating r, and elides those parts P
                  that are not relevant to estimating r. We desire that the SLI
                  transformation be both correct and efficient. By correct, we
                  mean that P and SLI(P) have identical estimates on r. By
                  efficient, we mean that estimation over SLI(P) be as small as
                  possible. We show that usual notions of program slicing, which
                  traverse control and data dependencies backward from the
                  return expression r, are unsatisfactory for probabilistic
                  programs, since it produces incorrect results on some programs
                  and inefficient results on others. Our key insight is that in
                  addition to the usual notions of control dependence and data
                  dependence that are used to slice non-probabilistic programs,
                  a new kind of dependence called observe dependence arises
                  naturally due to observe statements in probabilistic programs.
                  We propose a new definition of SLI(P) which is both correct
                  and efficient for probabilistic programs, by including observe
                  dependence in addition to control and data dependences for
                  computing slices. We prove correctness mathematically, and we
                  demonstrate efficiency empirically. We show that by applying
                  the SLI transformation as a pre-pass, we can improve the
                  efficiency of probabilistic inference, not only in our own
                  inference tool R2, but also in other systems for performing
                  inference such as Church and Infer.NET.},
author = {Hur, Chung-Kil and Nori, Aditya V. and Rajamani, Sriram K. and Samuel,
                  Selva},
doi = {10.1145/2594291.2594303},
isbn = {9781450327848},
journal = {Proc.~of PLDI},
keywords = {bayesian,probabilistic programming,program slicing},
pages = {133--144},
title = {{Slicing probabilistic programs}},
year = {2014}
}

@article{Carpenter2016,
author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matt and Lee, Daniel
                  and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus
                  A and Li, Peter and Riddell, Allen},
journal = {J. Statistical Software},
number = {Ii},
title = {Stan: A Probabilistic Programming Language},
volume = {VV},
doi={10.18637/jss.v076.i01},
year = {2016}
}

@article{Vlasselaer,
author = {Vlasselaer, Jonas and Renkens, Joris and Broeck, Guy Van Den and
                  Raedt, Luc De},
file = {:Users/sholtzen/Documents/Mendeley Desktop/Vlasselaer et
                  al/Unknown/plp{\_}sdd.pdf:pdf},
title = {{Compiling Probabilistic Logic Programs into Sentential Decision
                  Diagrams}},
year = {2015}
}


@article{Gordon2014,
abstract = {We propose a new kind of probabilistic programming language for
                  machine learning. We write programs simply by annotating
                  existing relational schemas with probabilistic model
                  expressions. We describe a detailed design of our language,
                  Tabular, complete with formal semantics and type system. A
                  rich series of examples illustrates the expressiveness of
                  Tabular. We report an implementation, and show evidence of the
                  succinctness of our notation relative to current best
                  practice. Finally, we describe and verify a transformation of
                  Tabular schemas so as to predict missing values in a concrete
                  database. The ability to query for missing values provides a
                  uniform interface to a wide variety of tasks, including
                  classification, clustering, recommendation, and ranking.},
author = {Gordon, Andrew D and Graepel, Thore and Rolland, Nicolas and Russo,
                  Claudio and Borgstrom, Johannes and Guiver, John},
doi = {10.1145/2535838.2535850},
isbn = {9781450325448},
issn = {15232867},
journal = {Proc.~of POPL},
keywords = {a column may have,a type such as,as well as scalar,bayesian
                  reasoning,link,machine
                  learning,model-learner,or,pattern,player 1
                  wins,players,probabilistic programming,relational
                  data,string,types such as bool,which,win1},
number = {1},
pages = {321--334},
title = {{Tabular: a schema-driven probabilistic programming language}},
year = {2014}
}

@inproceedings{Cousot1977,
 author = {Cousot, Patrick and Cousot, Radhia},
 title = {Abstract Interpretation: A Unified Lattice Model for Static Analysis
                  of Programs by Construction or Approximation of Fixpoints},
 booktitle = {Proc.~of POPL},
 year = {1977},
 location = {Los Angeles, California},
 pages = {238--252},
 numpages = {15},
 doi = {10.1145/512950.512973},
 acmid = {512973},
}

@article{Jhala2009,
abstract = {We survey recent progress in software model checking.},
author = {Jhala, Ranjit and Majumdar, Rupak},
doi = {10.1145/1592434.1592438},
file = {:Users/sholtzen/Documents/Mendeley Desktop/Jhala, Majumdar/ACM Computing
                  Surveys/Model-checking-Survey-a21-jhala.pdf:pdf},
isbn = {9783540331025},
issn = {03600300},
journal = {ACM Computing Surveys},
number = {4},
pages = {1--54},
title = {{Software model checking}},
volume = {41},
year = {2009}
}

@inproceedings{DeMoura2008,
 author = {De Moura, Leonardo and Bj{\o}rner, Nikolaj},
 title = {Z3: An Efficient SMT Solver},
 booktitle = {Proc.~of TACAS/ETAPS},
 year = {2008},
 isbn = {3-540-78799-2, 978-3-540-78799-0},
 location = {Budapest, Hungary},
 pages = {337--340},
 numpages = {4},
 acmid = {1792766},
 address = {Berlin, Heidelberg},
}


@INPROCEEDINGS{Goodman08,
    author = {Noah D. Goodman and Vikash K. Mansinghka and Daniel M. Roy and Keith Bonawitz and Joshua B. Tenenbaum},
    title = {Church: A language for generative models},
    booktitle = {Proc.~of UAI},
    year = {2008},
    pages = {220--229}
}


@book{polya,
  title={P{\'o}lya urn models},
  author={Mahmoud, Hosam},
  year={2008},
  publisher={Chapman and Hall/CRC}
}


@article{Raedt2007,
author = {{De Raedt}, Luc and Kimmig, Angelika and Toivonen, Hannu},
journal = {Proc.~of IJCAI},
keywords = {inductive logic programming,intelligent databases,probabilistic reasoning,theorem proving},
pages = {2468--2473},
title = {{ProbLog : A Probabilistic Prolog and Its Applications to Link}},
year = {2007}
}

@article{Morgan1996,
author = {Morgan, Carroll and McIver, Annabelle and Seidel, Karen},
doi = {10.1145/229542.229547},
issn = {01640925},
journal = {J. Programming Languages \& Systems},
number = {3},
pages = {325--353},
title = {{Probabilistic predicate transformers}},
volume = {18},
year = {1996}
}


@book{Pfeffer16,
 author = {Pfeffer, Avi},
 title = {Practical Probabilistic Programming},
 year = {2016},
 publisher = {Manning Publications},
} 



@article{Clark2007,
author = {Clark, David and Hunt, Sebastian and Malacaria, Pasquale},
journal = {J. Computer Security},
number = {3},
pages = {321--371},
title = {{A static analysis for quantifying information flow in a simple imperative language}},
volume = {15},
year = {2007}
}

@article{Gehr2016,
title = {PSI: Exact symbolic inference for probabilistic programs},
year = {2016},
author = {Gehr, Timon and Misailovic, Sasa and Vechev, Martin},
isbn = {9783319415277},
issn = {16113349},
journal = {Proc.~of ESOP/ETAPS},
pages = {62--83},
doi={10.1007/978-3-319-41528-4_4},
volume = {9779}
}

@inproceedings{KisaLTPM14,
  author = "Kisa, Doga and Van den Broeck, Guy and Choi, Arthur and Darwiche, Adnan",
  title = "Probabilistic sentential decision diagrams: {L}earning with massive logical constraints",
  booktitle = "ICML Workshop on Learning Tractable Probabilistic Models (LTPM)",
  location="Beijing, China",
  month = Jun,
  year = "2014",
  url = "http://starai.cs.ucla.edu/papers/KisaLTPM14.pdf",
  keywords   = {workshop}
}

@misc{PsiDirectory,
title = {PSI: Exact Symbolic Solver github},
year = {2022},
author = {Gehr, Timon and Misailovic, Sasa and Vechev, Martin},
url = {https://github.com/eth-sri/psi/tree/master/test}
}

@article{10.1145/367766.368168,
author = {Floyd, Robert W.},
title = {Algorithm 97: Shortest Path},
year = {1962},
issue_date = {June 1962},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {5},
number = {6},
issn = {0001-0782},
url = {https://doi.org/10.1145/367766.368168},
doi = {10.1145/367766.368168},
journal = {Commun. ACM},
month = {jun},
pages = {345},
numpages = {5}
}






@article{McCallum2009,
author = {McCallum, A and Schultz, K and Singh, S},
isbn = {9781615679119},
issn = {03643417},
journal = {Proc.~of NIPS},
pages = {1249--1257},
pmid = {12289432},
title = {{Factorie: Probabilistic programming via imperatively defined factor graphs}},
volume = {22},
year = {2009}
}

@book{Wasserman2010,
 author = {Wasserman, Larry},
 title = {All of Statistics: A Concise Course in Statistical Inference},
 year = {2010},
 isbn = {1441923225, 9781441923226},
 publisher = {Springer Publishing Company, Incorporated},
} 

inproceedings{Wood2014,
  author = {Wood, Frank and van de Meent, Jan Willem and Mansinghka, Vikash},
  booktitle = {Proc.~of AISTATS},
  title = {A New Approach to Probabilistic Programming Inference},
  pages = {1024-1032},
  year = {2014}
}


@article{Darwiche2011,
author = {Darwiche, Adnan},
doi = {10.5591/978-1-57735-516-8/IJCAI11-143},
isbn = {9781577355120},
issn = {10450823},
journal = {Proc.~of IJCAI},
keywords = {Knowledge Representation, Reasoning, and Logic},
pages = {819--826},
title = {{SDD: A new canonical representation of propositional knowledge bases}},
year = {2011}
}

@article{Kisa2014,
abstract = {We propose the Probabilistic Sentential Decision Diagram (PSDD): A
                  complete and canonical representation of probability
                  distributions defined over the models of a given propositional
                  theory. Each parameter of a PSDD can be viewed as the
                  (conditional) probability of making a decision in a
                  corresponding Sentential Decision Diagram (SDD). The SDD
                  itself is a recently proposed complete and canonical
                  representation of propositional theories. We explore a number
                  of interesting properties of PSDDs, including the in},
author = {Kisa, Doga and {Van den Broeck}, Guy},
journal = {Proc.~of KR},
title = {{Probabilistic Sentential Decision Diagrams}},
year = {2014}
}

@inproceedings{Chakraborty2013,
 author = {Chakraborty, Supratik and Meel, Kuldeep S. and Vardi, Moshe Y.},
 title = {A Scalable Approximate Model Counter},
 booktitle = {Proc.~of CP},
 year = {2013},
 pages = {200--216},
 numpages = {17},
} 


@inproceedings{Zhao2016,
  author    = {Shengjia Zhao and
               Sorathan Chaturapruek and
               Ashish Sabharwal and
               Stefano Ermon},
  title     = {Closing the Gap Between Short and Long XORs for Model Counting},
  booktitle = {Proc.~of AAAI},
  pages     = {3322--3329},
  year      = {2016},
}

@InBook{KY76,
author={D. Knuth and A. Yao},
title={Algorithms and Complexity: New Directions and Recent Results},
chapter={The complexity of nonuniform random number generation},
publisher={Academic Press},
year={1976},

volume={},
series={},
address={},
edition={},
month={},
note={},
key={}
}

@inproceedings{KNP11,
author={M. Kwiatkowska and G. Norman and D. Parker},
title={{PRISM} 4.0: Verification of Probabilistic Real-time Systems},
booktitle={Proc. 23rd International Conference on Computer Aided Verification (CAV'11)},
year={2011},

editor={G. Gopalakrishnan and S. Qadeer},
pages={585--591},
organization={},
publisher={Springer},
series={LNCS},
volume={6806},
address={},
month={},
note={},
key={}
}

@article{Darwiche_2002,
	doi = {10.1613/jair.989},
  
	url = {https://doi.org/10.1613%2Fjair.989},
  
	year = 2002,
	month = {sep},
  
	publisher = {{AI} Access Foundation},
  
	volume = {17},
  
	pages = {229--264},
  
	author = {A. Darwiche and P. Marquis},
  
	title = {A Knowledge Compilation Map},
  
	journal = {Journal of Artificial Intelligence Research}
}


@InProceedings{pmlr-v108-saad20a,
  title = 	 {The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions},
  author =       {Saad, Feras and Freer, Cameron and Rinard, Martin and Mansinghka, Vikash},
  booktitle = 	 {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
  pages = 	 {1036--1046},
  year = 	 {2020},
  editor = 	 {Chiappa, Silvia and Calandra, Roberto},
  volume = 	 {108},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--28 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v108/saad20a/saad20a.pdf},
  url = 	 {https://proceedings.mlr.press/v108/saad20a.html},
  abstract = 	 {This paper introduces a new algorithm for the fundamental problem of generating a random integer from a discrete probability distribution using a source of independent and unbiased random coin flips.  We prove that this algorithm, which we call the Fast Loaded Dice Roller (FLDR), is highly efficient in both space and time: (i) the size of the sampler is guaranteed to be linear in the number of bits needed to encode the input distribution; and (ii) the expected number of bits of entropy it consumes per sample is at most 6 bits more than the information-theoretically optimal rate.  We present fast implementations of the linear-time preprocessing and near-optimal sampling algorithms using unsigned integer arithmetic.  Empirical evaluations on a broad set of probability distributions establish that FLDR is 2x-10x faster in both preprocessing and sampling than multiple baseline algorithms, including the widely-used alias and interval samplers.  It also uses up to 10000x less space than the information-theoretically optimal sampler, at the expense of less than 1.5x runtime overhead.}
}

@book{Baier:2008:PMC:1373322,
 author = {Baier, Christel and Katoen, Joost-Pieter},
 title = {Principles of Model Checking (Representation and Mind Series)},
 year = {2008},
 isbn = {026202649X, 9780262026499},
 publisher = {The MIT Press},
} 

@book{Clarke2000,
 author = {Clarke,Jr., Edmund M. and Grumberg, Orna and Peled, Doron A.},
 title = {Model Checking},
 year = {1999},
 isbn = {0-262-03270-8},
 publisher = {MIT Press},
 address = {Cambridge, MA, USA},
}

@Inproceedings{Ball2000,
author="Ball, Thomas
and Rajamani, Sriram K.",
title="Bebop: A Symbolic Model Checker for Boolean Programs",
bookTitle="SPIN Model Checking and Software Verification",
year="2000",
pages="113--130",
}

@inproceedings{Donaldson2011,
  AUTHOR    = { Donaldson, Alastair
                and Kaiser, Alexander
                and Kroening, Daniel
                and Wahl, Thomas },
  TITLE     = { Symmetry-Aware Predicate Abstraction for Shared-Variable Concurrent Programs },
  BOOKTITLE = { Proc.~of CAV },
  YEAR      = { 2011 },
  PUBLISHER = { Springer },
  PAGES     = { 356--371 },
  SERIES    = { LNCS },
  VOLUME    = { 6806 },
}

@article{Valiant1979,
  author = {Valiant, Leslie G.},
  journal = {J. Theoretical Computer Science},
  keywords = {complexity counting permanent},
  pages = {189-201},
  title = {The Complexity of Computing the Permanent},
  volume = 8,
  year = 1979
}


@InCollection{GomesSS09,
  title =   "Model Counting",
  author =  "Carla P. Gomes and Ashish Sabharwal and Bart Selman",
  bibdate = "2017-05-16",
  bibsource =     "DBLP,
             http://dblp.uni-trier.de/https://doi.org/10.3233/978-1-58603-929-5-633;
             DBLP,
             http://dblp.uni-trier.de/db/series/faia/faia185.html#GomesSS09",
  booktitle =     "Handbook of Satisfiability",
  publisher =     "IOS Press",
  year =    "2009",
  volume =  "185",
  editor =  "Armin Biere and Marijn Heule and Hans van Maaren and
             Toby Walsh",
  ISBN =    "978-1-58603-929-5",
  pages =   "633--654",
  series =  "Frontiers in Artificial Intelligence and
             Applications",
}

@inproceedings{Monniaux2000,
 author = {Monniaux, David},
 title = {Abstract Interpretation of Probabilistic Semantics},
 booktitle = {International Symposium on Static Analysis},
 year = {2000},
 isbn = {3-540-67668-6},
 pages = {322--339},
 numpages = {18},
 acmid = {718164},
} 

@INPROCEEDINGS{Milch05,
    author = {Brian Milch and Bhaskara Marthi and Stuart Russell and David Sontag and Daniel L. Ong and Andrey Kolobov},
    title = {Blog: Probabilistic models with unknown objects},
    booktitle = {Proc.~of IJCAI},
    year = {2005},
    pages = {1352--1359}
}

@article{Fierens2015,
title={Inference and learning in probabilistic logic programs using weighted Boolean formulas},
author={Fierens, Daan and Van den Broeck, Guy and Renkens,
                  Joris and Shterionov, Dimitar and Gutmann, Bernd and Thon,
                  Ingo and Janssens, Gerda and De Raedt, Luc},
journal={J. Theory and Practice of Logic Programming}, year={2015},
pages = {358 - 401},
doi={10.1017/S1471068414000076},
volume = {15(3)}
}

@unpublished{EPIUGF2023,
title = {Exact Probabilistic Inference Using Generating Functions},
author = {Lutz Klinkenberg and Tobias Winkler and Mingshuai Chen and Joost-Pieter Katoen},
type = {Preprint},
year = {2023},
hideurl = { https://publications.rwth-aachen.de/record/862300},
}

@article{WEGENER2004229,
title = {BDDs—design, analysis, complexity, and applications},
journal = {Discrete Applied Mathematics},
volume = {138},
number = {1},
pages = {229-251},
year = {2004},
note = {Optimal Discrete Structures and Algorithms},
issn = {0166-218X},
doi = {https://doi.org/10.1016/S0166-218X(03)00297-X},
hideurl = {https://www.sciencedirect.com/science/article/pii/S0166218X0300297X},
author = {Ingo Wegener},
abstract = {BDDs (binary decision diagrams) and their variants are the most frequently used representation types or data structures for boolean functions. Research on BDD variants has turned out to be one of the areas where the symbiosis between theoretical investigations in algorithm design and analysis, complexity theory, and applications has led to progress in theory and in applications. Here the different roots of the interest in BDDs are described, the main BDD variants and their algorithmic properties are presented, the representation size of selected functions is investigated, lower bound techniques are discussed and applications to algorithmic graph problems and hardware verification problems are presented.}
}

@inproceedings{Michels2016,
 author = {Michels, Steffen and Hommersom, Arjen and Lucas, Peter J. F.},
 title = {Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming},
 booktitle = {Proc.~of IJCAI},
 year = 2016,
 pages = {3616--3622},
} 

@inproceedings{kersting2012lifted,
  title={Lifted probabilistic inference},
  author={Kersting, Kristian},
  booktitle={Proc.~of ECAI},
  pages={33--38},
  year={2012},
}

@inproceedings{HoltzenUAI17,
  author    = {Holtzen, Steven and Millstein, Todd and Van den Broeck, Guy},
  title     = {Probabilistic Program Abstractions},
  booktitle = {Proceedings of UAI},
  year={2017},
}

@article{Chavira2008,
 author = {Chavira, Mark and Darwiche, Adnan},
 title = {On Probabilistic Inference by Weighted Model Counting},
 journal = {J. Artificial Intelligence},
 issue_date = {April, 2008},
 volume = 172,
 number = {6-7},
 month = apr,
 year = 2008,
 issn = {0004-3702},
 pages = {772--799},
 numpages = 28,
 myurl = {http://dx.doi.org/10.1016/j.artint.2007.11.002},
 doi = {10.1016/j.artint.2007.11.002},
 acmid = 1342783,
 publisher = {Elsevier Science Publishers Ltd.},
 address = {Essex, UK},
 keywords = {Bayesian networks, Compilation, Exact inference, Weighted model counting},
} 


@inproceedings{Zhang:2017,
 author = {Zhang, Xin and Si, Xujie and Naik, Mayur},
 title = {Combining the Logical and the Probabilistic in Program Analysis},
 booktitle = {Proc.~ACM SIGPLAN International Workshop on Machine Learning and Programming Languages},
 year = {2017},
 isbn = {978-1-4503-5071-6},
 location = {Barcelona, Spain},
 pages = {27--34},
 numpages = {8},
 myurl = {http://doi.acm.org/10.1145/3088525.3088563},
 doi = {10.1145/3088525.3088563},
 acmid = {3088563},
 keywords = {Logic, Markov Logic Network, Maximum Satisfiability, Probability, Program Analysis},
} 

@inproceedings{Ball:2002,
 author = {Ball, Thomas and Rajamani, Sriram K.},
 title = {The {SLAM} Project: Debugging System Software via Static Analysis},
 booktitle = {Proc.~of POPL},
 year = {2002},
 isbn = {1-58113-450-9},
 location = {Portland, Oregon},
 pages = {1--3},
 numpages = {3},
 myurl = {http://doi.acm.org/10.1145/503272.503274},
 doi = {10.1145/503272.503274},
 acmid = {503274},
} 

@inproceedings{BelleIJCAI15,
  author    = {Belle, Vaishak and Passerini, Andrea and Van den Broeck, Guy},
  title     = {Probabilistic Inference in Hybrid Domains by Weighted Model Integration},
  booktitle = {Proc.~of IJCAI},
  year      = {2015},
  pages = {2770--2776},
 numpages = {7},

}

@inproceedings{BelleUAI15,
  author    = {Belle, Vaishak and Van den Broeck, Guy and Passerini, Andrea},
  title     = {Hashing-Based Approximate Probabilistic Inference in Hybrid Domains},
  booktitle= {Proc.~of UAI},
  year = {2015},
  pages = {141--150},
  numpages = {10},
}

@inproceedings{Holtzen2017,
  author = {Holtzen, Steven and Millstein, Todd and Van den Broeck, Guy},
  title = {Probabilistic Program Abstractions},
  year = {2017},
  booktitle = {Proc.~of UAI},
}

@inproceedings{Carbin2013,
 author = {Carbin, Michael and Misailovic, Sasa and Rinard, Martin C.},
 title = {Verifying Quantitative Reliability for Programs That Execute on Unreliable Hardware},
 booktitle = {Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages \&\#38; Applications},
 series = {OOPSLA '13},
 year = {2013},
 isbn = {978-1-4503-2374-1},
 location = {Indianapolis, Indiana, USA},
 pages = {33--52},
 numpages = {20},
 myurl = {http://doi.acm.org/10.1145/2509136.2509546},
 doi = {10.1145/2509136.2509546},
 acmid = {2509546},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {approximate computing},
} 

@inproceedings{Sculley2014,
title = {Machine Learning: The High Interest Credit Card of Technical Debt},
author  = {D. Sculley and Gary Holt and Daniel Golovin and Eugene Davydov and Todd Phillips and Dietmar Ebner and Vinay Chaudhary and Michael Young},
year  = 2014,
booktitle = {SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)}
}


@InProceedings{Selsam2017,
  title = 	 {Developing Bug-Free Machine Learning Systems With Formal Mathematics},
  author = 	 {Daniel Selsam and Percy Liang and David L. Dill},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {3047--3056},
  year = 	 {2017},
  editor = 	 {Doina Precup and Yee Whye Teh},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {International Convention Centre, Sydney, Australia},
  month = 	 {06--11 Aug},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/selsam17a/selsam17a.pdf},
  myurl = 	 {http://proceedings.mlr.press/v70/selsam17a.html},
  abstract = 	 {Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a formal theorem defining what it means for their system to be correct. The process of proving this theorem interactively in the proof assistant exposes all implementation errors since any error in the program would cause the proof to fail. As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i.e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients. We train a variational autoencoder using Certigrad and find the performance comparable to training the same model in TensorFlow.}
}

@Inbook{Hermanns2008,
author="Hermanns, Holger
and Wachter, Bj{\"o}rn
and Zhang, Lijun",
editor="Gupta, Aarti
and Malik, Sharad",
title="Probabilistic CEGAR ",
bookTitle="Computer Aided Verification: 20th International Conference, CAV 2008 Princeton, NJ, USA, July 7-14, 2008 Proceedings",
year="2008",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="162--175",
abstract="Counterexample-guided abstraction refinement (CEGAR) has been en vogue for the automatic verification of very large systems in the past years. When trying to apply CEGAR to the verification of probabilistic systems, various foundational questions arise. This paper explores them in the context of predicate abstraction.",
isbn="978-3-540-70545-1",
doi="10.1007/978-3-540-70545-1_16",
myhideurl       ="https://doi.org/10.1007/978-3-540-70545-1_16"
}

@inproceedings{Kozen1979,
 author = {Kozen, Dexter},
 title = {Semantics of Probabilistic Programs},
 booktitle = {Proceedings of the 20th Annual Symposium on Foundations of Computer Science},
 series = {SFCS '79},
 year = {1979},
 pages = {101--114},
 numpages = {14},
 myurl = {http://dx.doi.org/10.1109/SFCS.1979.38},
 doi = {10.1109/SFCS.1979.38},
 acmid = {1382610},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
}

@inproceedings{Giry1981,
  title = {A categorical approach to probability theory},
  year = {1981},
  author = {Giry, M.},
  booktitle = {Categorical Aspects of Topology and Analysis},
  volume = {915},
  publisher = {Springer-Verlag}
}

@inproceedings{Jones1989,
 author = {Jones, C. and Plotkin, G.},
 title = {A Probabilistic Powerdomain of Evaluations},
 booktitle = {Proceedings of the Fourth Annual Symposium on Logic in Computer Science},
 year = {1989},
 isbn = {0-8186-1954-6},
 location = {Pacific Grove, California, USA},
 pages = {186--195},
 numpages = {10},
 myurl = {http://dl.acm.org/citation.cfm?id=77350.77370},
 acmid = {77370},
 publisher = {IEEE Press},
 address = {Piscataway, NJ, USA},
}

@inproceedings{Djahromi1980,
  author = {Saheb-Djahromi, Nasser},
  title = {CPO's of measures for non-determinism},
  booktitle = {Theoretical Computer Science},
  year = {1980},
  volume = {12(1)},
  pages = {19--37}
}

@article{Clarke2003,
 author = {Clarke, Edmund and Grumberg, Orna and Jha, Somesh and Lu, Yuan and Veith, Helmut},
 title = {Counterexample-guided Abstraction Refinement for Symbolic Model Checking},
 journal = {J. ACM},
 issue_date = {September 2003},
 volume = {50},
 number = {5},
 month = sep,
 year = {2003},
 issn = {0004-5411},
 pages = {752--794},
 numpages = {43},
 myurl = {http://doi.acm.org/10.1145/876638.876643},
 doi = {10.1145/876638.876643},
 acmid = {876643},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Abstraction, hardware verification, symbolic model checking, temporal logic},
} 

@inproceedings{Kwiatkowska2011,
author = {Kwiatkowska, Marta and Norman, Gethin and Parker, David},
title = {PRISM 4.0: Verification of Probabilistic Real-time Systems},
booktitle = {Proceedings of the 23rd International Conference on Computer Aided Verification},
series = {CAV'11},
year = {2011},
isbn = {978-3-642-22109-5},
location = {Snowbird, UT},
pages = {585--591},
numpages = {7},
myurl = {http://dl.acm.org/citation.cfm?id=2032305.2032352},
acmid = {2032352},
doi={10.1007/978-3-642-22110-1_47},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
}

@inproceedings{Smolka2017,
 author = {Smolka, Steffen and Kumar, Praveen and Foster, Nate and Kozen, Dexter and Silva, Alexandra},
 title = {Cantor Meets Scott: Semantic Foundations for Probabilistic Networks},
 booktitle = {Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages},
 series = {POPL 2017},
 year = {2017},
 isbn = {978-1-4503-4660-3},
 location = {Paris, France},
 pages = {557--571},
 numpages = {15},
 url = {http://doi.acm.org/10.1145/3009837.3009843},
 doi = {10.1145/3009837.3009843},
 acmid = {3009843},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {D.3.1 [Programming Lan- guages]: Formal Definitions and Theory\'{z}Semantics Kleene algebra with tests, Domain theory, NetKAT, Probabilistic semantics, Software-defined networking},
} 


@inproceedings{Huang2016,
 author = {Huang, Daniel and Morrisett, Greg},
 title = {An Application of Computable Distributions to the Semantics of Probabilistic Programming Languages},
 booktitle = {Proceedings of the 25th European Symposium on Programming Languages and Systems - Volume 9632},
 year = {2016},
 isbn = {978-3-662-49497-4},
 pages = {337--363},
 numpages = {27},
 url = {https://doi.org/10.1007/978-3-662-49498-1_14},
 doi = {10.1007/978-3-662-49498-1_14},
 acmid = {3089542},
 publisher = {Springer-Verlag New York, Inc.},
 address = {New York, NY, USA},
 keywords = {Computable distributions, Probabilistic programs, Semantics},
} 

@inproceedings{Foster2016,
 author = {Foster, Nate and Kozen, Dexter and Mamouras, Konstantinos and Reitblatt, Mark and Silva, Alexandra},
 title = {Probabilistic NetKAT},
 booktitle = {Proceedings of the 25th European Symposium on Programming Languages and Systems - Volume 9632},
 year = {2016},
 isbn = {978-3-662-49497-4},
 pages = {282--309},
 numpages = {28},
 url = {http://dx.doi.org/10.1007/978-3-662-49498-1_12},
 doi = {10.1007/978-3-662-49498-1_12},
 acmid = {2958892},
 publisher = {Springer-Verlag New York, Inc.},
 address = {New York, NY, USA},
} 

@misc{InferNET14, 
author = "Minka, T. and Winn, J.M. and Guiver, J.P. and Webster, S. and Zaykov, Y. and Yangel, B. and Spengler, A. and  Bronskill, J.",
title = {{Infer.NET 2.6}},
year = 2014,
note = {Microsoft Research Cambridge. http://research.microsoft.com/infernet}
}

@article{tran2016edward,
  author = {Dustin Tran and Alp Kucukelbir and Adji B. Dieng and Maja Rudolph and Dawen Liang and David M. Blei},
  title = {{Edward: A library for probabilistic modeling, inference, and criticism}},
  journal = {arXiv preprint arXiv:1610.09787},
  year = {2016}
}

@article{Pfeffer2007,
author = {Pfeffer, Avi},
file = {:Users/sholtzen/Downloads/61ec5d60ba91a2a78758f07bc10610997e57.pdf:pdf},
isbn = {0262072882},
journal = {Introduction to statistical relational learning},
number = {1993},
pages = {399},
title = {{The Design and Implementation of IBAL: A General-Purpose Probabilistic Language}},
year = {2007}
}

@article{Hur2015,
author = {Hur, Chung-kil and Nori, Aditya V. and Rajamani, Sriram K. and Sammuel, Selva},
doi = {10.4230/LIPIcs.FSTTCS.2015.475},
file = {:Users/sholtzen/Downloads/final.pdf:pdf},
isbn = {9783939897972},
issn = {18688969},
journal = {FSTTCS},
keywords = {2015,4230,and phrases probabilistic programming,digital object identifier 10,ence,fsttcs,lipics,markov chain monte carlo,p,probabilistic infer-,program correctness,sampling},
number = {FSTTCS},
pages = {1--14},
title = {{A Provably Correct Sampler for Probabilistic Programs}},
year = {2015}
}

@inproceedings{10.5555/2969033.2969117,
author = {Tristan, Jean-Baptiste and Huang, Daniel and Tassarotti, Joseph and Pocock, Adam and Green, Stephen J. and Steele, Guy L.},
title = {Augur: Data-Parallel Probabilistic Modeling},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
abstract = {Implementing inference procedures for each new probabilistic model is time-consuming and error-prone. Probabilistic programming addresses this problem by allowing a user to specify the model and then automatically generating the inference procedure. To make this practical it is important to generate high performance inference code. In turn, on modern architectures, high performance requires parallel execution. In this paper we present Augur, a probabilistic modeling language and compiler for Bayesian networks designed to make effective use of data-parallel architectures such as GPUs. We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
pages = {2600–2608},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14}
}



@article{Albarghouthi2017_1,
 author = {Albarghouthi, Aws and D'Antoni, Loris and Drews, Samuel and Nori, Aditya V.},
 title = {FairSquare: Probabilistic Verification of Program Fairness},
 journal = {Proc. ACM Program. Lang.},
 issue_date = {October 2017},
 volume = 1,
 number = {OOPSLA},
 month = oct,
 year = 2017,
 issn = {2475-1421},
 pages = {80:1--80:30},
 articleno = 80,
 numpages = 30,
 url = {http://doi.acm.org/10.1145/3133904},
 doi = {10.1145/3133904},
 acmid = 3133904,
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Algorithmic Fairness, Probabilistic Inference, Probabilistic Programming},
} 




@inproceedings{Kisynski2009IJCAI,
	Author = {Kisynski, Jacek and Poole, David},
	Booktitle = {Proceedings of IJCAI},
	Pages = {1922--1929},
	Title = {Lifted Aggregation in Directed First-order Probabilistic Models},
	Year = {2011}}

@article{dillon2017tensorflow,
  title={TensorFlow Distributions},
  author={Dillon, Joshua V and Langmore, Ian and Tran, Dustin and Brevdo, Eugene and Vasudevan, Srinivas and Moore, Dave and Patton, Brian and Alemi, Alex and Hoffman, Matt and Saurous, Rif A},
  journal={arXiv preprint arXiv:1711.10604},
  year={2017}
}




@inproceedings{naesseth2017reparameterization,
  title={Reparameterization gradients through acceptance-rejection sampling algorithms},
  author={Naesseth, Christian and Ruiz, Francisco and Linderman, Scott and Blei, David},
  booktitle={Artificial Intelligence and Statistics},
  pages={489--498},
  year={2017}
}


@inproceedings{probhack,
  title={Towards PPL: Extending Coroutines in Hack to Enable Probabilistic Programming},
  author={Michael Tingley},
  booktitle={SPLASH-I},
  year={2017}
}

@article{KANTAS2009774,
title = {An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models},
journal = {IFAC Proceedings Volumes},
volume = {42},
number = {10},
pages = {774-785},
year = {2009},
note = {15th IFAC Symposium on System Identification},
issn = {1474-6670},
doi = {https://doi.org/10.3182/20090706-3-FR-2004.00129},
hideurl = {https://www.sciencedirect.com/science/article/pii/S1474667016387432},
author = {N. Kantas and A. Doucet and S.S. Singh and J.M. Maciejowski},
keywords = {parameter estimation, general state-space models, hidden Markov models, sequential Monte Carlo},
abstract = {Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods.}
}




@article{Diez2003IJIS,
	Author = {D{\'\i}ez, Francisco Javier and Gal{\'a}n, Severino F.},
	Journal = {International Journal of Intelligent Systems},
	Number = {2},
	Pages = {165--177},
	Title = {Efficient computation for the noisy MAX},
	Volume = {18},
	Year = {2003}}

@inproceedings{pfeffer2018structured,
  title={Structured Factored Inference for Probabilistic Programming},
  author={Pfeffer, Avi and Ruttenberg, Brian and Kretschmer, William and OConnor, Alison},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1224--1232},
  year={2018}
}

@InProceedings{pmlr-v32-paige14,
  title = 	 {A Compilation Target for Probabilistic Programming Languages},
  author = 	 {Brooks Paige and Frank Wood},
  booktitle = 	 {Proceedings of the 31st International Conference on Machine Learning},
  pages = 	 {1935--1943},
  year = 	 {2014},
  editor = 	 {Eric P. Xing and Tony Jebara},
  volume = 	 {32},
  number =       {2},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Bejing, China},
  month = 	 {22--24 Jun},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v32/paige14.pdf},
  url = 	 {http://proceedings.mlr.press/v32/paige14.html},
  abstract = 	 {Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory.   Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target.  This opens up a new hardware and systems research path for optimizing probabilistic programming systems.}
}

@article{milch2012general,
  title={General-purpose MCMC inference over relational structures},
  author={Milch, Brian and Russell, Stuart},
  journal={arXiv preprint arXiv:1206.6849},
  year={2012}
}

@article{li2015swift,
  title={SWIFT: Compiled inference for probabilistic programs},
  author={Li, Lei and Wu, Yi and Russell, Stuart J},
  journal={EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2015-12},
  year={2015}
}

@article{mansinghka2014venture,
  title={Venture: a higher-order probabilistic programming platform with programmable inference},
  author={Mansinghka, Vikash and Selsam, Daniel and Perov, Yura},
  journal={arXiv preprint arXiv:1404.0099},
  year={2014}
}

@article{tran2017deep,
  title={Deep probabilistic programming},
  author={Tran, Dustin and Hoffman, Matthew D and Saurous, Rif A and Brevdo, Eugene and Murphy, Kevin and Blei, David M},
  journal={arXiv preprint arXiv:1701.03757},
  year={2017}
}

@inproceedings{Vomlel2008PGM,
	Author = {Vomlel, J. and Savicky, P.},
	Booktitle = {Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM 2008), Hirtshals, Denmark},
	Pages = {297--304},
	Title = {Arithmetic circuits of the noisy-or models},
	Year = {2008}}

@article{Jha2012VLDBE,
	Author = {Jha, Abhay and Suciu, Dan},
	Journal = {Proceedings of the VLDB Endowment},
	Number = {11},
	Pages = {1160--1171},
	Title = {Probabilistic databases with {MarkoViews}},
	Volume = {5},
	Year = {2012}}

@incollection{tseitin1983complexity,
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  publisher={Springer}
}

@inproceedings{chanrobustness,
  title={On the robustness of Most Probable Explanations},
  author={Chan, Hei and Darwiche, Adnan},
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  year={2006},
}


        
@article{Chavira2006,
        Author = {Chavira, Mark and Darwiche, Adnan and Jaeger, Manfred},
        Journal = {IJAR},
        Keywords = {bayesian networks,exact inference,relational models},
        Month = may,
        Number = {1-2},
        Pages = {4--20},
        Title = {Compiling Relational {Bayesian} Networks for Exact Inference},
        Volume = {42},
        Year = {2006}}


        


@article{darwiche2002logical,
  title={A logical approach to factoring belief networks},
  author={Darwiche, Adnan},
  journal={Proceedings of KR},
  pages={409--420},
  year={2002},
}

@incollection{friedman1998learning,
  title={Learning Bayesian networks with local structure},
  author={Friedman, Nir and Goldszmidt, Moises},
  booktitle={Learning in graphical models},
  pages={421--459},
  year={1998},
  publisher={Springer}
}

@article{richardson2006markov,
  title={Markov logic networks},
  author={Richardson, Matthew and Domingos, Pedro},
  journal={Machine learning},
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  pages={107--136},
  year={2006},
  publisher={Springer}
}


@inproceedings{CeylanKR16,
  author = {Ismail Ilkan Ceylan and Adnan Darwiche and Guy {Van den Broeck}},
  title = {Open-World Probabilistic Databases},
  booktitle = {Proceedings of KR},
  year = {2016},
  keywords   = {conference,strong,selected}
}


@article{suciu2011probabilistic,
  title={Probabilistic Databases},
  author={Suciu, Dan and Olteanu, Dan and R{\'e}, Christopher and Koch, Christoph},
  journal={Synthesis Lectures on Data Management},
  volume={3},
  number={2},
  pages={1--180},
  year={2011},
  publisher={Morgan \& Claypool Publishers}
}


@inproceedings{Poole2003,
  Author = {David Poole},
  Booktitle = {Proc.~IJCAI},
  Title = {First-order probabilistic inference.},
  Year = {2003}
}

@inproceedings{Fierens11,
  author = "Fierens, Daan and Van den Broeck, Guy and Thon, Ingo and Gutmann, Bernd and De Raedt, Luc",
  title = "Inference in probabilistic logic programs using weighted {CNF}'s",
  booktitle = "Proceedings of UAI",
  pages = "211--220",
  month = Jul,
  year = "2011",
}


@inproceedings{ChoiKisaDarwiche13,
author = {Arthur Choi and Doga Kisa and Adnan Darwiche},
title = {Compiling Probabilistic Graphical Models using Sentential Decision Diagrams},
booktitle = {Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)},
year = {2013},
}

@inproceedings{de2007problog,
  title={ProbLog: A Probabilistic Prolog and Its Application in Link Discovery},
  author={De Raedt, Luc and Kimmig, Angelika and Toivonen, Hannu},
  booktitle={Proceedings of IJCAI},
  volume={7},
  pages={2462--2467},
  year={2007}
}

@book{Getoor07:book,
        Date-Added = {2011-01-24 10:24:26 +0100},
        Date-Modified = {2011-01-24 10:24:26 +0100},
        Editor = {Getoor, L. and Taskar, B.},
        Publisher = {{MIT} {P}ress},
        Title = {An Introduction to Statistical Relational Learning},
        Year = {2007}}

@book{DeRaedt2008-PILP,
        editor = {De Raedt, Luc and Frasconi, Paolo and Kersting, Kristian and Muggleton, Stephen},
        title = {Probabilistic inductive logic programming: theory and applications},
        year = {2008},
        isbn = {3-540-78651-1, 978-3-540-78651-1},
        publisher = {Springer-Verlag},
        address = {Berlin, Heidelberg}
}

@inproceedings{haddawy1994generating,
  title={Generating {Bayesian} networks from probability logic knowledge bases},
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  booktitle={Proceedings of UAI},
  pages={262--269},
  year={1994},
  organization={Morgan Kaufmann Publishers Inc.}
}

@article{braz2016probabilistic,
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  year={2016}
}

@INPROCEEDINGS{gogatePTP,
AUTHOR = "Vibhav Gogate and Pedro Domingos",
TITLE = "Probabilistic Theorem Proving",
BOOKTITLE = "Proceedings of UAI",
YEAR = "2011",
PAGES = "256--265"
}


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@article{kimmig2012algebraic,
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  year={2012}
}

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}


@inproceedings{VdBAAAI12,
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  title = "Conditioning in first-order knowledge compilation and lifted probabilistic inference",
  booktitle = "Proceedings of the 26th Conference on Artificial Intelligence (AAAI)",
  year = "2012",
}


@inproceedings{VdBNIPS11,
  author = "{Van den Broeck}, Guy",
  title = "On the completeness of first-order knowledge compilation for lifted probabilistic inference",
  booktitle = "Advances in Neural Information Processing Systems 24 (NIPS)",
  pages = "1386--1394",
  year = "2011",
}

        
@inproceedings{JaegerStarAI12,
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  booktitle = "Proceedings of StarAI",
  year = "2012",
}


@techreport{hinrichs2006herbrand,
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  year="2006",
}

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@inproceedings{Cozman2004ECAI,
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	Year = {2004}}


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  pages={358},
  year={2004}
}

@inproceedings {Kersting:2012,
author = {Kersting, Kristian},
title = {Lifted Probabilistic Inference},
year = {2012},
booktitle = {Proceedings of European Conference on Artificial Intelligence (ECAI)},
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@inproceedings{gogate2012advances,
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  author={Gogate, Vibhav and Jha, Abhay Kumar and Venugopal, Deepak},
  booktitle={Proceedings of AAAI},
  pages = {1910--1916},
  year={2012}
}


@phdthesis{VdBThesis,
  author = "Van den Broeck, Guy",
  title = "Lifted {I}nference and {L}earning in {S}tatistical {R}elational {M}odels ",
  school = "KU Leuven",
  pages = "xx + 264",
  month = Jan,
  year = "2013",
}

@inproceedings{VdBDarwiche13,
author = {Guy {Van den Broeck} and Adnan Darwiche},
title = {On the Complexity and Approximation of Binary Evidence in Lifted Inference},
booktitle = {Advances in Neural Information Processing Systems 26 (NIPS)},
location = "Lake Tahoe, USA",
year = {2013},
}


@inproceedings{venugopal2014evidence,
  title={Evidence-Based Clustering for Scalable Inference in Markov Logic},
  author={Venugopal, Deepak and Gogate, Vibhav},
  booktitle={ECML PKDD},
  pages={258--273},
  year={2014},
}

@article{jaeger2012lower,
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  author={Jaeger, Manfred},
  journal={arXiv preprint arXiv:1204.3255},
  year={2012}
}

@inproceedings{van2012conditioning,
  title={Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference.},
  author={Van den Broeck, Guy and Davis, Jesse},
  booktitle={Proceedings of AAAI},
  year={2012}
}

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@inproceedings{beame2013model,
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  year={2013}
}

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@inproceedings { ahmadi12ecmlpkdd,
author = {B. Ahmadi and K. Kersting and S. Natarajan},
title = {Lifted Online Training of Relational Models with Stochastic Gradient Methods},
year = {2012},
booktitle = {Proceedings of ECML PKDD},
}


@inproceedings{LiftedStructure,
  author = "Van Haaren, Jan and Van den Broeck, Guy and Meert, Wannes and Davis, Jesse",
  title = "Tractable learning of liftable {M}arkov logic networks",
  booktitle = "LTPM",
  month = Jun,
  year = "2014",
  myurl = "https://lirias.kuleuven.be/handle/123456789/455406",
}


@inproceedings{singla2014approximate,
  title={Approximate Lifting Techniques for Belief Propagation},
  author={Singla, Parag and Nath, Aniruddh and Domingos, Pedro},
  year={2014},
  booktitle = "Proceedings of AAAI",
}



@inproceedings{mladenovlifted,
  title={Lifted Message Passing as Reparametrization of Graphical Models},
  author={Mladenov, Martin and Globerson, Amir and Kersting, Kristian},
  booktitle={UAI},
  year={2014}
}

@inproceedings{niepert2013symmetry,
  title={Symmetry-Aware Marginal Density Estimation},
  author={Niepert, Mathias},
  booktitle={Proceedings of AAAI},
  year={2013}
}

@inproceedings{bui2014lifted,
  title={Lifted Tree-Reweighted Variational Inference},
  author={Bui, Hung Hai and Huynh, Tuyen N and Sontag, David},
  booktitle={Proceedings of UAI},
  year={2014}
}



@article{broeck2013skolemization,
  title={Skolemization for weighted first-order model counting},
  author={Van den Broeck, Guy and Meert, Wannes and Darwiche, Adnan},
  journal={Proceedings of UAI},
  year={2013}
}

@article{gribkoff2014lifted,
  title={Lifted Probabilistic Inference: A Guide for the Database Researcher},
  author={Gribkoff, Eric and Suciu, Dan and Van den Broeck, Guy},
  year={2014},
  journal={Bulletin of the Technical Committee on Data Engineering},
}

@inproceedings{FierensNips,
  author = "Fierens, Daan and Van den Broeck, Guy and Bruynooghe, Maurice and De Raedt, Luc",
  title = "Constraints for probabilistic logic programming",
  booktitle = "Proceedings of the NIPS Probabilistic Programming Workshop",
  year = "2012",
}

@article{niepert2014tractability,
  title={Tractability through exchangeability: A new perspective on efficient probabilistic inference},
  author={Niepert, Mathias and Van den Broeck, Guy},
  journal={Proceedings of AAAI},
  year={2014}
}

@article{milch2007blog,
  title={BLOG: Probabilistic Models with Unknown Objects},
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  journal={Statistical relational learning},
  pages={373},
  year={2007}
}

@inproceedings{VdBAAAI15a,
  author    = {Guy {Van den Broeck} and
               Mathias Niepert},
  title     = {Lifted Probabilistic Inference for Asymmetric Graphical Models},
  booktitle = {Proceedings of AAAI},
  year = {2015},
  myurl = {http://arxiv.org/pdf/1412.0315v1.pdf},
}

@inproceedings{milch2005blog,
  title={{BLOG}: Probabilistic Models with Unknown Objects},
  author={Milch, B. and Marthi, B.kara and Russell, S. and Sontag, D. and Ong, D. L. and Kolobov, A.},
  booktitle={IJCAI},  
  pages={1352--1359},
  year={2005}
}

@inproceedings{ChoiVdBD15,
  author    = {Choi, A. and {Van den Broeck}, G. and Darwiche, A.},
  title     = {A Tractable Framework for Learning and Querying Preference Distributions},
  year      = {2015},
  note      = {Under review}
}


@inproceedings{xue12,
  author    = {X. Xue and A. Choi and S. Darwiche},
  title     = {Basing Decisions on Sentences in Decision Diagrams},
  booktitle = {In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI)},
  year      = {2012}
}

@inproceedings{ChoiDarwiche13,
author = {Choi, A. and Darwiche, A.},
title = {Dynamic Minimization of Sentential Decision Diagrams},
booktitle = {Proceedings AAAI},
year = {2013},
}

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  title={Knowledge compilation and theory approximation},
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  pages={193--224},
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}

@comment{x-kbibtex-personnameformatting=<%l><, %f>}

@incollection{Poole08,
	author = "David~Poole",
	booktitle = "{Probabilistic Inductive Logic Programming}",
	crossref = "DeRaedt07-APRIL",
	pages = "222--243",
	title = "{The Independent Choice Logic and Beyond}",
	year = 2008
}



@inproceedings{Milch2005IJCAI,
	author = "Milch, Brian and Marthi, Bhaskara and Russell, Stuart J. and Sontag, David and Ong, Daniel L. and Kolobov, Andrey",
	bibsource = "dblp computer science bibliography, http://dblp.org",
	booktitle = "{Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI)}",
	timestamp = "Wed, 14 Dec 2005 11:15:39 +0100",
	title = "{{BLOG:} Probabilistic Models with Unknown Objects}",
	myurl = "http://www.ijcai.org/papers/1546.pdf; http://dblp.uni-trier.de/rec/bib/conf/ijcai/MilchMRSOK05",
	year = "2005"
}

@article{merrellweighted,
  title={Weighted Model Integration with Orthogonal Transformations},
  author={Merrell, David and Albarghouthi, Aws and D’Antoni, Loris}
}

@article{Dean:1989:MRP:86605.86609,
	acmid = "86609",
	address = "Cambridge, MA, USA",
	author = "Dean, Thomas and Kanazawa, Keiji",
	doi = "10.1111/j.1467-8640.1989.tb00324.x",
	issn = "0824-7935",
	issue_date = "Aug. 1990",
	journal = "Comput. Intell.",
	month = dec,
	number = "3",
	numpages = "9",
	pages = "142--150",
	publisher = "Blackwell Publishers, Inc.",
	title = "{A model for reasoning about persistence and causation}",
	myurl = "http://dx.doi.org/10.1111/j.1467-8640.1989.tb00324.x",
	volume = "5",
	year = "1989"
}

@inproceedings{kwisthout2010necessity,
  title={The Necessity of Bounded Treewidth for Efficient Inference in Bayesian Networks.},
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  pages={237--242},
  year={2010}
}


@article{darwiche2001recursive,
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  volume={126},
  number={1-2},
  pages={5--41},
  year={2001},
  publisher={Elsevier}
}

@phdthesis{murphy02,
	added-at = "2009-09-10T14:36:22.000+0200",
	author = "Murphy, Kevin",
	bibmyurl = "http://www.bibsonomy.org/bibtex/27fef11e7786fdd23cb4a1145347113f8/gregoryy",
	interhash = "f1eb8d23313c740e97b4f0fbaca0df28",
	intrahash = "7fef11e7786fdd23cb4a1145347113f8",
	keywords = "ai ml; stat",
	month = jul,
	owner = "heinrich",
	school = "UC Berkeley, Computer Science Division",
	timestamp = "2009-09-10T14:36:22.000+0200",
	title = "{Dynamic Bayesian Networks: Representation, Inference and Learning}",
	year = "2002"
}

@article{wainwright2008graphical,
  title={Graphical models, exponential families, and variational inference},
  author={Wainwright, Martin J and Jordan, Michael I and others},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={1},
  number={1--2},
  pages={1--305},
  year={2008},
  publisher={Now Publishers, Inc.}
}

@incollection{sontag2011introduction,
  title={Introduction to dual composition for inference},
  author={Sontag, David and Globerson, Amir and Jaakkola, Tommi},
  booktitle={Optimization for Machine Learning},
  year={2011},
  publisher={MIT Press}
}

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@article{citeulike:777801,
	abstract = "{Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efciently and accurately is difcult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to rst-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We rst extend the Rao-Blackwellised particle ltering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle ltering. The rst one uses abstraction hierarchies over the predicates to smooth the particle lter's estimates. The second employs kernel density estimation with a kernel function specically designed for relational domains. Experiments show these two methods greatly outperform standard particle ltering on the task of assembly plan execution monitoring.}",
	author = "Sanghai, Sumit and Domingos, Pedro and Weld, Daniel",
	citeulike-article-id = "777801",
	citeulike-linkout-0 = "http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume24/sanghai05a.pdf",
	journal = "Journal of Artificial Intelligence Research",
	keywords = "bayesian; dbn; relational",
	pages = "759--797",
	posted-at = "2006-07-28 13:03:13",
	priority = "4",
	title = "{Relational Dynamic Bayesian Networks}",
	myurl = "http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume24/sanghai05a.pdf",
	volume = "24",
	year = "2005"
}

@inproceedings{Thon2008MLG,
	author = "Thon, Ingo and Landwehr, Niels and {De Raedt}, Luc",
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	title = "{{CPT-L}: An efficient model for relational stochastic processes}",
	year = "2008"
}

@article{Domingos2011CoRR,
	author = "Domingos, Pedro and Sanghai, Sumit K. and Weld, Daniel S.",
	journal = "CoRR",
	title = "{Relational Dynamic Bayesian Networks}",
	volume = "abs/1109.2137",
	year = "2011"
}

@incollection{Manfredotti2009AAI,
	author = "Manfredotti, Cristina",
	booktitle = "{Advances in artificial intelligence}",
	pages = "287--290",
	publisher = "Springer",
	title = "{Modeling and inference with relational dynamic Bayesian networks}",
	year = "2009"
}

@book{Nilsson:1995:LPP:546470,
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	author = "Nilsson, Ulf and Maluszynski, Jan",
	edition = "2nd",
	isbn = "0471959960",
	publisher = "John Wiley \& Sons, Inc.",
	title = "{Logic, Programming, and PROLOG}",
	year = "1995"
}

@book{lloyd:book89,
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	edition = "2.",
	publisher = "Springer",
	title = "{Foundations of Logic Programming}",
	year = 1989
}

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	author = "Bancilhon, Francois and Maier, David and Sagiv, Yehoshua and {Jeffrey D. Ullman}",
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	doi = "10.1145/6012.15399",
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	publisher = "ACM",
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	year = 1986
}

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@inproceedings{nori2014r2,
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@inproceedings{chaganty2013efficiently,
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  year={2013}
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@article{kimmig2011implementation,
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@article{kiselyovmetropolis,
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@inproceedings{kiselyov2009embedded,
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@inproceedings{kiselyov2016probabilistic,
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@inproceedings{ritchie2016c3,
  title={C3: Lightweight incrementalized MCMC for probabilistic programs using continuations and callsite caching},
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@article{mansinghka2014venture,
  title={Venture: a higher-order probabilistic programming platform with programmable inference},
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@article{wingate2013automated,
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  year={2013}
}

@inproceedings{wood2014new,
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  year={2014}
}

@article{luttinen2016bayespy,
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  pages={1419--1424},
  year={2016},
  publisher={JMLR. org}
}

@inproceedings{van2015particle,
  title={Particle Gibbs with Ancestor Sampling for Probabilistic Programs},
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  booktitle={AISTATS},
  year={2015}
}

@inproceedings{wingate2011lightweight,
  title={Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation.},
  author={Wingate, David and Stuhlm{\"u}ller, Andreas and Goodman, Noah D},
  booktitle={AISTATS},
  pages={770--778},
  year={2011}
}

inproceedings{wood2014new,
  title={A New Approach to Probabilistic Programming Inference.},
  author={Wood, Frank and van de Meent, Jan-Willem and Mansinghka, Vikash},
  booktitle={AISTATS},
  pages={1024--1032},
  year={2014}
}

@article{tran2016edward,
  title={Edward: A library for probabilistic modeling, inference, and criticism},
  author={Tran, Dustin and Kucukelbir, Alp and Dieng, Adji B and Rudolph, Maja and Liang, Dawen and Blei, David M},
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  year={2016}
}

@article{lake2015human,
  title={Human-level concept learning through probabilistic program induction},
  author={Lake, Brenden M and Salakhutdinov, Ruslan and Tenenbaum, Joshua B},
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  year={2015},
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}

@InProceedings{10.1007/978-3-030-17462-0_5,
author="Brain, Martin
and Schanda, Florian
and Sun, Youcheng",
editor="Vojnar, Tom{\'a}{\v{s}}
and Zhang, Lijun",
title="Building Better Bit-Blasting for Floating-Point Problems",
booktitle="Tools and Algorithms for the Construction and Analysis of Systems",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="79--98",
abstract="An effective approach to handling the theory of floating-point is to reduce it to the theory of bit-vectors. Implementing the required encodings is complex, error prone and requires a deep understanding of floating-point hardware. This paper presents SymFPU, a library of encodings that can be included in solvers. It also includes a verification argument for its correctness, and experimental results showing that its use in CVC4 out-performs all previous tools. As well as a significantly improved performance and correctness, it is hoped this will give a simple route to add support for the theory of floating-point.",
isbn="978-3-030-17462-0"
}

@InProceedings{10.1007/978-3-030-81688-9_7,
author="Shi, Xiaomu
and Fu, Yu-Fu
and Liu, Jiaxiang
and Tsai, Ming-Hsien
and Wang, Bow-Yaw
and Yang, Bo-Yin",
editor="Silva, Alexandra
and Leino, K. Rustan M.",
title="CoqQFBV: A Scalable Certified SMT Quantifier-Free Bit-Vector Solver",
booktitle="Computer Aided Verification",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="149--171",
abstract="We present a certified SMT  QF{\_}BV solver CoqQFBV built from a verified bit blasting algorithm, Kissat, and the verified SAT certificate checker GratChk in this paper. Our verified bit blasting algorithm supports the full QF{\_}BV logic of SMT-LIB; it is specified and formally verified in the proof assistant Coq . We compare CoqQFBV with CVC4, Bitwuzla, and Boolector on benchmarks from the QF{\_}BV division of the single query track in the 2020 SMT Competition, and real-world cryptographic program verification problems. CoqQFBV surprisingly solves more program verification problems with certification than the 2020 SMT  QF{\_}BV division winner Bitwuzla without certification.",
isbn="978-3-030-81688-9"
}

@misc{pi,
  author = {@10kdiver},
  title = {@10kdiver},
  howpublished = "\url{https://twitter.com/10kdiver/status/1503307755976765443?s=20&t=Ux1C55thW5qnCPVOHoxguQ}",
  year = {2022}, 
  note = "[Online; accessed 14-March-2022]"
}

@misc{https://doi.org/10.48550/arxiv.2204.02948,
  doi = {10.48550/ARXIV.2204.02948},
  
  url = {https://arxiv.org/abs/2204.02948},
  
  author = {Beutner, Raven and Ong, Luke and Zaiser, Fabian},
  
  keywords = {Programming Languages (cs.PL), Machine Learning (cs.LG), Logic in Computer Science (cs.LO), Computation (stat.CO), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, F.3.1; F.3.2; D.2.4; G.3},
  
  title = {Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@inproceedings{narayanan2016probabilistic,
    title = {Probabilistic inference by program transformation in Hakaru (system description)},
    author = {Narayanan, Praveen and Carette, Jacques and Romano, Wren and Shan, Chung{-}chieh and Zinkov, Robert},
    booktitle = {International Symposium on Functional and Logic Programming - 13th International Symposium, {FLOPS} 2016, Kochi, Japan, March 4-6, 2016, Proceedings},
    pages = {62--79},
    year = {2016},
    organization = {Springer},
    url = {http://dx.doi.org/10.1007/978-3-319-29604-3_5},
    doi = {10.1007/978-3-319-29604-3_5},
}

@article{ritchie2015controlling,
  title={Controlling procedural modeling programs with stochastically-ordered sequential monte carlo},
  author={Ritchie, Daniel and Mildenhall, Ben and Goodman, Noah D and Hanrahan, Pat},
  journal={ACM Transactions on Graphics (TOG)},
  volume={34},
  number={4},
  pages={105},
  year={2015},
  publisher={ACM}
}

@inproceedings{gordon2014tabular,
  title={Tabular: a schema-driven probabilistic programming language},
  author={Gordon, Andrew D and Graepel, Thore and Rolland, Nicolas and Russo, Claudio and Borgstrom, Johannes and Guiver, John},
  booktitle={ACM SIGPLAN Notices},
  volume={49},
  number={1},
  pages={321--334},
  year={2014},
  organization={ACM}
}

@inproceedings{mansinghka2013approximate,
  title={Approximate bayesian image interpretation using generative probabilistic graphics programs},
  author={Mansinghka, Vikash and Kulkarni, Tejas D and Perov, Yura N and Tenenbaum, Josh},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1520--1528},
  year={2013}
}

@article{menon2013machine,
  title={A Machine Learning Framework for Programming by Example.},
  author={Menon, Aditya Krishna and Tamuz, Omer and Gulwani, Sumit and Lampson, Butler W and Kalai, Adam},
  journal={ICML (1)},
  volume={28},
  pages={187--195},
  year={2013}
}

@inproceedings{wingate2011lightweight,
  title={Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation.},
  author={Wingate, David and Stuhlm{\"u}ller, Andreas and Goodman, Noah D},
  booktitle={AISTATS},
  pages={770--778},
  year={2011}
}

@inproceedings{gordon2014probabilistic,
  title={Probabilistic programming},
  author={Gordon, Andrew D and Henzinger, Thomas A and Nori, Aditya V and Rajamani, Sriram K},
  booktitle={Proceedings of the on Future of Software Engineering},
  pages={167--181},
  year={2014},
  organization={ACM}
}

@article{pfeffer2009figaro,
  title={Figaro: An object-oriented probabilistic programming language},
  author={Pfeffer, Avi},
  journal={Charles River Analytics Technical Report},
  volume={137},
  year={2009}
}

@inproceedings{Sato:95,
	author = "Taisuke~Sato",
	booktitle = "{Proceedings of the 12th International Conference on Logic Programming (ICLP)}",
	title = "{A statistical learning method for logic programs with distribution semantics}",
	year = 1995
}

@inproceedings{DeRaedt07-IJCAIa,
	author = "{De Raedt}, Luc~ and Angelika~Kimmig and Hannu~Toivonen",
	booktitle = "{Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI)}",
	date-added = "2007-08-09 16:43:51 +0200",
	date-modified = "2008-02-04 20:12:43 +0100",
	title = "{Prob{L}og: A Probabilistic {P}rolog and Its Application in Link Discovery.}",
	year = 2007
}

@book{DeRaedt07-APRIL,
	date-added = "2013-11-13 13:23:39 +0000",
	date-modified = "2013-11-13 13:23:39 +0000",
	editor = "{De Raedt}, L. and Frasconi, P. and Kersting, K. and Muggleton, S.",
	publisher = "Springer",
	series = "{Lecture Notes in Artificial Intelligence}",
	title = "{Probabilistic Inductive Logic Programming --- Theory and Applications}",
	volume = 4911,
	year = 2008
}

@article{SatoKameya:01,
	author = "T.~Sato and Y.~Kameya",
	bibsource = "DBLP, http://dblp.uni-trier.de",
	ee = "http://www.cs.washington.edu/research/jair/abstracts/sato01a.html",
	journal = "J. Artif. Intell. Res. (JAIR)",
	pages = "391--454",
	title = "{Parameter Learning of Logic Programs for Symbolic-Statistical Modeling}",
	volume = 15,
	year = 2001
}

@article{Riguzzi2011TPLP,
	author = "Riguzzi, Fabrizio and Swift, Terrance",
	journal = "Theory and Practice of Logic Programming",
	number = "4--5",
	pages = "433--449",
  doi={10.1017/S147106841100010X},
	title = "{The {PITA} System: Tabling and Answer Subsumption for Reasoning under Uncertainty}",
	volume = "11",
	year = "2011"
}

@inproceedings{Darwiche2011JCAI,
	author = "Darwiche, Adnan",
	booktitle = "{Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI)}",
	title = "{{SDD}: A New Canonical Representation of Propositional Knowledge Bases}",
	year = "2011"
}

@misc{t2015stan,
  key={Stan Modeling Language},
  title={Stan Modeling Language: User’s Guide and Reference Manual},
  year={2015},
}

@article{307537,
	author = "Gutmann, Bernd and Thon, Ingo and Kimmig, Angelika and Bruynooghe, Maurice and {De Raedt}, Luc",
	doi = "10.1017/S1471068411000238",
	journal = "Theory and Practice of Logic Programming",
	pages = "663--680",
	title = "{The magic of logical inference in probabilistic programming}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/307537",
	volume = "11",
	year = "2011"
}

@inproceedings{minka2001expectation,
  title={Expectation propagation for approximate Bayesian inference},
  author={Minka, Thomas P},
  booktitle={Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence},
  pages={362--369},
  year={2001},
  organization={Morgan Kaufmann Publishers Inc.}
}

@inproceedings{451332,
	author = "Renkens, Joris and Kimmig, Angelika and {Van den Broeck}, Guy and {De Raedt}, Luc",
	booktitle = "{Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI)}",
	title = "{Explanation-based approximate weighted model counting for probabilistic logics}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/451332",
	year = "2014"
}

@article{citeulike:2174422,
	abstract = "{Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein--protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver.We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99\%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.Availability: The algorithm and data are available at http://cs.tau.ac.il/\~{}roded/SPINE.htmlContact: roded@post.tau.ac.il}",
	author = "Ourfali, Oved and Shlomi, Tomer and Ideker, Trey and Ruppin, Eytan and Sharan, Roded",
	booktitle = "{Bioinformatics}",
	citeulike-article-id = "2174422",
	citeulike-linkout-0 = "http://dx.doi.org/10.1093/bioinformatics/btm170",
	citeulike-linkout-1 = "http://bioinformatics.oxfordjournals.org/content/23/13/i359.abstract",
	citeulike-linkout-2 = "http://bioinformatics.oxfordjournals.org/content/23/13/i359.full.pdf",
	citeulike-linkout-3 = "http://bioinformatics.oxfordjournals.org/cgi/content/abstract/23/13/i359",
	citeulike-linkout-4 = "http://view.ncbi.nlm.nih.gov/pubmed/17646318",
	citeulike-linkout-5 = "http://www.hubmed.org/display.cgi?uids=17646318",
	day = "1",
	doi = "10.1093/bioinformatics/btm170",
	issn = "1460-2059",
	journal = "Bioinformatics",
	keywords = "biological\_networks; causality; signaling\_nw",
	number = "13",
	pages = "359--366",
	pmid = "17646318",
	posted-at = "2009-02-28 19:46:21",
	priority = "2",
	title = "{SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments}",
	myurl = "http://dx.doi.org/10.1093/bioinformatics/btm170",
	volume = "23",
	year = "2007"
}


@article{de2013phenetic,
  title={PheNetic: network-based interpretation of unstructured gene lists in E. coli},
  author={De Maeyer, Dries and Renkens, Joris and Cloots, Lore and {De Raedt}, Luc and Marchal, Kathleen},
  journal={Molecular BioSystems},
  volume={9},
  number={7},
  pages={1594--1603},
  year={2013},
  publisher={Royal Society of Chemistry}
}

@article{Poole93ngc,
	author = "Poole, David",
	date-added = "2008-06-13 11:10:22 +0200",
	date-modified = "2008-06-13 11:10:22 +0200",
	journal = "New {G}eneration {C}omputing",
	pages = "377--400",
	title = "{Logic {p}rogramming, {a}bduction and {p}robability}",
	volume = "11",
	year = "1993"
}

@article{353833,
	author = "Renkens, Joris and {Van den Broeck}, Guy and Nijssen, Siegfried",
	doi = "10.1007/s10994-012-5304-9",
	journal = "Machine Learning",
	number = "3",
	pages = "215--231",
	title = "{k-optimal: {A} novel approximate inference algorithm for {P}rob{L}og}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/353833",
	volume = "89",
	year = "2012"
}

@inproceedings{Poon:2006:SEI:1597538.1597612,
	acmid = "1597612",
	author = "Poon, Hoifung and Domingos, Pedro",
	booktitle = "{Proceedings of the 21st National Conference on Artificial Intelligence (AAAI)}",
	numpages = "6",
	title = "{Sound and Efficient Inference with Probabilistic and Deterministic Dependencies}",
	myurl = "http://dl.acm.org/citation.cfm?id=1597538.1597612",
	year = "2006"
}

@inproceedings{kucukelbir2015automatic,
  title={Automatic variational inference in Stan},
  author={Kucukelbir, Alp and Ranganath, Rajesh and Gelman, Andrew and Blei, David},
  booktitle={Advances in neural information processing systems},
  pages={568--576},
  year={2015}
}

@inproceedings{goodman:uai08,
	author = "Goodman, Noah D. and Mansinghka, Vikash K. and Roy, Daniel M. and Bonawitz, Keith and Tenenbaum, Joshua B.",
	bibsource = "dblp computer science bibliography, http://dblp.org",
	booktitle = "{Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (UAI)}",
	timestamp = "Thu, 15 Dec 2011 17:17:44 +0100",
	title = "{Church: a language for generative models}",
	myurl = "http://uai.sis.pitt.edu/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=1346&proceeding_id=24; http://dblp.uni-trier.de/rec/bib/conf/uai/GoodmanMRBT08",
	year = "2008"
}

@inproceedings{aziz:aaai15,
	author = "Aziz, Rehan Abdul and Chu, Geoffrey and Muise, Christian and Stuckey, Peter J.",
	booktitle = "{Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI)}",
	title = "{Stable Model Counting and Its Application in Probabilistic Logic Programming}",
	year = "2015"
}

@article{Kimmig11tplp,
	author = "Kimmig, Angelika and Demoen, Bart and {De Raedt}, Luc and {Santos Costa}, V{\'i}tor and Rocha, Ricardo",
	editors = "Mar{\'i}a Garc{\'i}a de la Banda and Enrico Pontelli",
	issue = "2-3",
	journal = "Theory and Practice of Logic Programming (TPLP)",
	pages = "235--262",
	title = "{On the Implementation of the Probabilistic Logic Programming Language {P}rob{L}og}",
	volume = "11",
	year = 2011
}

@article{riguzzi:fi13,
	author = "Riguzzi, Fabrizio",
	bibsource = "DBLP, http://dblp.uni-trier.de",
	date-added = "2013-12-03 16:12:06 +0000",
	date-modified = "2013-12-03 16:12:06 +0000",
	ee = "http://dx.doi.org/10.3233/FI-2013-847",
	journal = "Fundam. Inform.",
	number = "4",
	pages = "521--541",
	title = "{MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming}",
	volume = "124",
	year = "2013"
}

@inproceedings{Davis:Domingos:09,
	abstract = "Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of second-order Markov logic. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge between molecular biology, social network and Web domains. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, like various forms of homophily.",
	added-at = "2009-06-11T15:38:44.000+0200",
	author = "Davis, Jesse and Domingos, Pedro",
	booktitle = "{Proceedings of the 26th International Conference on Machine Learning (ICML)}",
	interhash = "398a3d68f44a5295ccf8e09b9736d2e3",
	intrahash = "31d9d2bba9b8df065b6efdf5fdf25c41",
	keywords = "2009 icml markovlogic multitask",
	timestamp = "2009-06-11T15:38:44.000+0200",
	title = "{Deep Transfer via Second-Order Markov Logic}",
	myurl = "http://www.cs.mcgill.ca/~icml2009/papers/561.pdf; http://www.bibsonomy.org/bibtex/231d9d2bba9b8df065b6efdf5fdf25c41/seandalai",
	year = 2009
}

@article{VanEmden76a,
	abstract = "{Sentences in first-order predicate logic can be usefully interpreted as programs In this paper the operational and fixpomt semantics of predicate logic programs are defined, and the connections with the proof theory and model theory of logic are investigated It is concluded that operational semantics is a part of proof theory and that fixpolnt semantics is a special case of model-theoret:c semantics KEY WORDS AND PHRASES predicate logic as a programming language, semantics of programming languages, resolution theorem proving, operaUonal versus denotatlonal semantics, SL-resoluuon, flxpomt characteriza-tion}",
	added-at = "2011-05-04T16:04:17.000+0200",
	author = "{Van Emden}, Maarten. H. and Kowalski, Robert. A.",
	bibmyurl = "http://www.bibsonomy.org/bibtex/25515b2a60f23a23ce8084c00114f8d9a/baisemain",
	citeulike-article-id = "6236007",
	citeulike-linkout-0 = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.9246",
	interhash = "9f5652cc85b1d8b05f1b4f038d85a245",
	intrahash = "5515b2a60f23a23ce8084c00114f8d9a",
	journal = "Journal of the ACM",
	keywords = "*cite logic-programming; semantics",
	pages = "569--574",
	posted-at = "2009-11-28 19:13:20",
	priority = "2",
	timestamp = "2011-05-04T16:04:17.000+0200",
	title = "{The Semantics of Predicate Logic as a Programming Language}",
	myurl = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.9246",
	volume = 23,
	year = 1976
}

@inproceedings{conf/ecai/Darwiche04,
	added-at = "2004-09-02T00:00:00.000+0200",
	author = "Darwiche, Adnan",
	booktitle = "{Proceedings of the 16th European Conference on Artificial Intelligence (ECAI)}",
	date = "2004-09-02",
	description = "dblp",
	interhash = "7a9cff41a94b412f1302d1e71b4100a2",
	intrahash = "7f775b562f17eb2cef3c547270d6fa41",
	isbn = "1-58603-452-9",
	keywords = "dblp",
	timestamp = "2004-09-02T00:00:00.000+0200",
	title = "{New Advances in Compiling CNF into Decomposable Negation Normal Form.}",
	myurl = "http://dblp.uni-trier.de/db/conf/ecai/ecai2004.html#Darwiche04; http://www.bibsonomy.org/bibtex/27f775b562f17eb2cef3c547270d6fa41/dblp",
	year = 2004
}

@article{510960,
	author = "Vlasselaer, Jonas and Meert, Wannes and {Van den Broeck}, Guy and {De Raedt}, Luc",
	journal = "Artificial Intelligence",
	month = mar,
	pages = "43--53",
	title = "{Exploiting local and repeated structure in dynamic {B}ayesian networks}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/510960",
	volume = "232",
	year = "2016"
}

@article{nitti2016probabilistic,
  title={Probabilistic logic programming for hybrid relational domains},
  author={Nitti, Davide and De Laet, Tinne and De Raedt, Luc},
  journal={Machine Learning},
  volume={103},
  number={3},
  pages={407--449},
  year={2016},
  publisher={Springer}
}

@inproceedings{Vlasselaer2015,
	author = "Vlasselaer, Jonas and {Van den Broeck}, Guy and Kimmig, Angelika and Meert, Wannes and {De Raedt}, Luc",
	booktitle = "{Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI)}",
	month = jul,
	title = "{Anytime inference in probabilistic logic programs with {T}p-compilation}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/494681",
  doi={10.1016/j.ijar.2016.06.009},
	year = "2015"
}

@inproceedings{415987,
	author = "Nitti, Davide and {De Laet}, Tinne and {De Raedt}, Luc",
	booktitle = "{IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}",
	doi = "10.1109/IROS.2013.6696747",
	title = "{A particle filter for hybrid relational domains}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/415987",
	year = "2013"
}

@article{VanGelder:1991:WSG:116825.116838,
	acmid = "116838",
	address = "New York, NY, USA",
	author = "{Van Gelder}, Allen and Ross, Kenneth A. and Schlipf, John S.",
	doi = "10.1145/116825.116838",
	issn = "0004-5411",
	issue_date = "July 1991",
	journal = "J. ACM",
	keywords = "fixpoints; negation as failure; stable models; three-valued logic; unfounded sets; well-founded models",
	month = jul,
	number = "3",
	numpages = "31",
	pages = "619--649",
	publisher = "ACM",
	title = "{The Well-founded Semantics for General Logic Programs}",
	myurl = "http://doi.acm.org/10.1145/116825.116838",
	volume = "38",
	year = "1991"
}

@article{502798,
	author = "Bogaerts, Bart and {Van den Broeck}, Guy",
	doi = "10.1017/S1471068415000162",
	journal = "Theory and Practice of Logic Programming",
	number = "4-5",
	pages = "464--480",
	title = "{Knowledge compilation of logic programs using approximation fixpoint theory}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/502798",
	volume = "15",
	year = "2015"
}

@article{Clark1978LDB,
	author = "Clark, Keith L",
	journal = "Logic and databases",
	pages = "293--322",
	title = "{Negation as failure}",
	year = "1978"
}

@inproceedings{448017,
	author = "Nitti, Davide and {De Laet}, Tinne and {De Raedt}, Luc",
	booktitle = "{IEEE International Conference on Robotics and Automation (ICRA)}",
	doi = "10.1109/ICRA.2014.6906966",
	title = "{Relational object tracking and learning}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/448017",
	year = "2014"
}

@inproceedings{Riguzzi07,
	author = "Riguzzi, Fabrizio",
	booktitle = "{Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence (AI*IA)}",
	title = "{A Top Down Interpreter for {LPAD} and CP-Logic}",
	year = "2007"
}

@article{272983,
	author = "Thon, Ingo and Niels, Landwehr and {De Raedt}, Luc",
	doi = "10.1007/s10994-010-5213-8",
	journal = "Machine Learning",
	month = feb,
	number = "2",
	pages = "239--272",
	title = "{Stochastic relational processes: {E}fficient inference and applications}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/272983",
	volume = "82",
	year = "2011"
}

@article{490338,
	author = "{De Raedt}, Luc and Kimmig, Angelika",
	doi = "10.1007/s10994-015-5494-z",
	journal = "Machine Learning",
	number = "1",
	pages = "5--47",
	title = "{Probabilistic (logic) programming concepts}",
	myurl = "https://lirias.kuleuven.be/handle/123456789/490338",
	volume = "100",
	year = "2015"
}

@book{Getoor07:book,
	date-added = "2014-01-30 08:51:10 +0000",
	date-modified = "2014-01-30 08:51:10 +0000",
	editor = "Getoor, L. and Taskar, B.",
	publisher = "{MIT} {P}ress",
	title = "{An Introduction to Statistical Relational Learning}",
	year = "2007"
}

@book{pfeffer2014ppp,
	author = "Pfeffer, Avi",
	publisher = "Manning Publications",
	title = "{Practical Probabilistic Programming}",
	year = "2014"
}

@book{Suciu:2011:PD:2031527,
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@string{uai03={Proceedings of the {N}ineteenth {C}onference on {U}ncertainty
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@string{pami={IEEE Transactions on Pattern Analysis and Machine Intelligence}}
@string{mk={Morgan Kaufmann}}
@string{acm={ACM Press}}
@string{mlc05={Proceedings of the {T}wenty {S}econd {I}nternational {C}onference on
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@string{mlc95={Proceedings of the {T}welfth {I}nternational {C}onference on
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@string{uci={Department of Information and Computer Science, University of
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@string{aaai={AAAI Press}}
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}
@inProceedings{narasimhan2004-paclearn,
   author =     {M. Narasimhan and J. Bilmes},
   title =      {{PAC}-learning bounded tree-width Graphical Models},
   booktitle =  {Proc.\ UAI},
   year =      2004,
}

@inproceedings{pipatsrisawat2010top,
  title={Top-Down Algorithms for Constructing Structured DNNF: Theoretical and Practical Implications},
  author={Pipatsrisawat, Knot and Darwiche, Adnan},
  booktitle={ECAI},
  pages={3--8},
  year={2010}
}

@inproceedings{jebara2012perfect,
  title={Perfect graphs and graphical modeling},
  author={Jebara, Tony},
  booktitle={Tractability, Cambridge University Press},
  year={2012},
}

@inproceedings{murphy&al99,
    author = "K. P. Murphy and Y. Weiss and M. I. Jordan",
    title = "{{Loopy Belief Propagation for Approximate Inference: An Empirical Study}}",
    booktitle = {Proceedings of UAI},
    year = 1999
}

@book{murphy2012machine,
  title={Machine learning: a probabilistic perspective},
  author={Murphy, Kevin P},
  year={2012},
  publisher={MIT press}
}

@article{bayati2008exactness,
  title={On the exactness of the cavity method for weighted b-matchings on arbitrary graphs and its relation to linear programs},
  author={Bayati, Mohsen and Borgs, Christian and Chayes, Jennifer and Zecchina, Riccardo},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  volume={2008},
  number={06},
  pages={L06001},
  year={2008},
  publisher={IOP Publishing}
}

@inproceedings{dennis2012learning,
  title={Learning the architecture of sum-product networks using clustering on variables},
  author={Dennis, Aaron and Ventura, Dan},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2033--2041},
  year={2012}
}

@inproceedings{poon2011sum,
  title={Sum-product networks: A new deep architecture},
  author={Poon, Hoifung and Domingos, Pedro},
  booktitle={Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
  pages={689--690},
  year={2011},
  organization={IEEE}
}

@article{besag1975statistical,
  title={Statistical analysis of non-lattice data},
  author={Besag, Julian},
  journal={The statistician},
  pages={179--195},
  year={1975},
  publisher={JSTOR}
}

@inproceedings{lowd:uai08,
title = "Learning Arithmetic Circuits",
author="D. Lowd and P. Domingos",
booktitle={Proceedings of UAI},
pages="383-392",
year = "2008",
}

@inproceedings{hulten&domingos02b,
author={G. Hulten and P. Domingos},
title={Mining Complex Models from Arbitrarily Large Databases in Constant
  Time},
booktitle=kdd02,
year={2002},
pages={525-531},
address={Edmonton, Canada},
publisher=acm
}

@techreport{ucirep00,
author={C. Blake and C. J. Merz},
title={{UCI} Repository of Machine Learning Databases},
institution={Dept. ICS, UC Irvine, CA},
year={2000},
note={http://\-www.ics.uci.edu/\-$\sim$mlearn/\-MLRepository.html}
}
@article{kohavi&al00,
author={R. Kohavi and C. Brodley and B. Frasca and L. Mason
  and Z. Zheng},
title={{KDD-Cup} 2000 Organizers' Report:  Peeling the Onion},
journal={SIGKDD Explorations},
volume={2},
number={2},
pages={86-98},
year={2000}
}

@inproceedings{DBLP:conf/sdm/BrombergMH06,
  author    = {Facundo Bromberg and
               Dimitris Margaritis and
               Vasant Honavar},
  title     = {Efficient Markov Network Structure Discovery using Independence
               Tests},
  booktitle = {Proceedings of the Sixth SIAM International Conference on
               Data Mining},
  year      = {2006},
}

@inproceedings{DBLP:conf/uai/GanapathiVDK08,
  author    = {Varun Ganapathi and
               David Vickrey and
               John Duchi and
               Daphne Koller},
  title     = {Constrained Approximate Maximum Entropy Learning of Markov
               Random Fields},
  booktitle = { Proceedings of the 24th Conference in Uncertainty
               in Artificial Intelligence},
  year      = {2008},
publisher = "AUAI Press",
  pages     = {196-203},
}

@book{gilks&al96,
author={W. R. Gilks and S. Richardson and D. J. Spiegelhalter},
title={{M}arkov Chain {M}onte {C}arlo in Practice},
publisher={Chapman and Hall},
year={1996},
}
@incollection{kulesza2008,
 title = {{Structured Learning with Approximate Inference}},
 author = {A. Kulesza and F. Pereira},
 booktitle = {{Advances in Neural Information Processing Systems 20}},
 pages = {785--792},
 year = {2007},
 publisher = {MIT Press},
}

@incollection{lee2006,
 title = {{Efficient Structure Learning of Markov Networks using $L_1$-Regularization}},
 author = {Su-In Lee and Varun Ganapathi and Daphne Koller},
 booktitle = {{Advances in Neural Information Processing Systems 19}},
 pages = {817--824},
 year = {2007},
 publisher = {MIT Press},
}

@proceedings{DBLP:conf/nips/2007,
  editor    = {John C. Platt and
               Daphne Koller and
               Yoram Singer and
               Sam T. Roweis},
  title     = {Advances in Neural Information Processing Systems 20, Proceedings
               of the Twenty-First Annual Conference on Neural Information
               Processing Systems, Vancouver, British Columbia, Canada,
               December 3-6, 2007},
  booktitle = {NIPS},
  publisher = {MIT Press},
  year      = {2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
@inproceedings{cohen95,
author={W. W. Cohen},
title={Fast Effective Rule Induction},
booktitle="Proc. ICML'95",
year={1995},
pages={115-123},
}
@inproceedings{poon&domingos06,
author={H. Poon and P. Domingos},
title={Sound and Efficient Inference with Probabilistic and Deterministic
  Dependencies},
booktitle= "Proc. AAAI'06",
year={2006},
pages={458-463},
}
@inproceedings{DBLP:conf/uai/WexlerM08,
  author    = {Y. Wexler and  C. Meek},
  title     = {Inference for Multiplicative Models},
  booktitle = {Proc. UAI'08},
  year      = {2008},
  pages     = {595-602}
}

@inproceedings{andrew:icml07,
  title="{{Scalable Training of L1-Regularized Log-Linear Models}}",
  author={Andrew, G. and Gao, J.},
  booktitle={Proceedings of the Twenty-Fourth International Conference on Machine Learning},
  pages={33--40},
  year={2007},
  organization=acm
}


@inproceedings{vanhaaren:aaai12,
  title="{{Markov Network Structure Learning: A Randomized Feature Generation Approach}}",
  author={Van Haaren, J. and Davis, J.},
  booktitle={Proceedings of the Twenty-Sixth AAAI Conference on Artificial
               Intelligence},
  year={2012},
  organization=AAAI
}

@proceedings{DBLP:conf/uai/2008,
  editor    = {David A. McAllester and
               Petri Myllym{\"a}ki},
  title     = {UAI 2008, Proceedings of the 24th Conference in Uncertainty
               in Artificial Intelligence, July 9-12, 2008, Helsinki, Finland},
  booktitle = {UAI},
  publisher = {AUAI Press},
  year      = {2008},
  isbn      = {0-9749039-4-9},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
@article{goldberg&al01,
year={2001},
title={Eigentaste: A constant time collaborative filtering algorithm},
author={K. Goldberg and T. Roeder and D. Gupta and C. Perkins},
journal={Information Retrieval},
volume=4,
number=2,
pages={133--151},
publisher={Springer}
}

@incollection{saad2016,
author      = {Feras Saad and Vikash Mansinghka},
title       = {A Probabilistic Programming Approach To Probabilistic Data Analysis},
year        = {2016},
booktitle   = {Advances in Neural Information Processing Systems (NIPS)},
myurl_paper   = {https://papers.nips.cc/paper/6060-a-probabilistic-programming-approach-to-probabilistic-data-analysis.pdf},
abstract    = {Probabilistic techniques are central to data analysis, but
  different approaches can be challenging to apply, combine, and compare. This
  paper introduces composable generative population models (CGPMs), a
  computational abstraction that extends directed graphical models and can be
  used to describe and compose a broad class of probabilistic data analysis
  techniques. Examples include discriminative machine learning, hierarchical
  Bayesian models, multivariate kernel methods, clustering algorithms, and
  arbitrary probabilistic programs. We demonstrate the integration of CGPMs into
  BayesDB, a probabilistic programming platform that can express data analysis
  tasks using a modeling definition language and structured query language. The
  practical value is illustrated in two ways. First, the paper describes an
  analysis on a database of Earth satellites, which identifies records that
  probably violate Kepler’s Third Law by composing causal probabilistic programs
  with nonparametric Bayes in 50 lines of probabilistic code. Second, it reports
  the lines of code and accuracy of CGPMs compared with baseline solutions from
  standard machine learning libraries.},
}

@inproceedings{ziegler:05,
author = "C. Ziegler and S. McNee and J. Konstan and G. Lausen",
title = "Improving Recommendation Lists Through Topic Diversification",
booktitle = "Proc. WWW'05",
pages     = {22-32},
year = 2005
}
%; Proceedings of the 14th International World Wide Web Conference (WWW '05), May 10-14, 2005, Chiba, Japan. To appear.

@inproceedings{kulkarni2015picture,
  title={Picture: A probabilistic programming language for scene perception},
  author={Kulkarni, Tejas D and Kohli, Pushmeet and Tenenbaum, Joshua B and Mansinghka, Vikash},
  booktitle={Proceedings of the ieee conference on computer vision and pattern recognition},
  pages={4390--4399},
  year={2015}
}

@article{yang:02,
author = "M. Yang and H. Goldstein and W. Browne and G. Woodhouse",
title = "Multivariate multilevel analyses of examination results",
journal = "Journal of the Royal Statistics Society, Series A, Statistics in Society",
volume = 165,
year = 2002,
pages = "137 - 153"
}
@string{mlc10={Proceedings of the {T}wenty-{S}eventh {I}nternational
  {C}onference on {M}achine {L}earning}}

@inproceedings{davis&domingos10,
author={J. Davis and P. Domingos},
title={{Bottom-Up Learning of {M}arkov Network Structure}},
booktitle=mlc10,
year={2010},
publisher=acm
}

@TechReport{WinMine,
  author =       {D. M. Chickering},
  title =        {The {WinMine} Toolkit},
  institution =  {Microsoft},
  year =         2002,
  number =       {MSR-TR-2002-103},
  address =      {Redmond, WA}
}

@inproceedings{chickering&al97,
author={D. Chickering and D. Heckerman and C. Meek},
title={A {Bayesian} Approach to Learning {Bayesian} Networks with Local Structure},
booktitle={Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence},
address={Providence, RI},
pages={80-89},
publisher={Morgan Kaufmann},
year=1997
}

@string{jmlr={Journal of Machine Learning Research}}
@article{heckerman&al00,
author={D. Heckerman and D. M. Chickering and C. Meek and R. Rounthwaite
  and C. Kadie},
title={Dependency Networks for Inference, Collaborative Filtering,
  and Data Visualization},
journal=jmlr,
year={2000},
volume={1},
pages={49-75}
}

@string{kdd00={Proceedings of the {S}ixth {ACM} {SIGKDD} {I}nternational
  {C}onference on {K}nowledge {D}iscovery and {D}ata {M}ining}}

@inproceedings{domingos&hulten00,
author={P. Domingos and G. Hulten},
title={Mining High-Speed Data Streams},
booktitle=kdd00,
year={2000},
pages={71-80},
address={Boston, MA},
publisher=acm
}

@inproceedings{lowd:icdm10,
author ={D. Lowd and J. Davis},
title = "{{Learning Markov Network Structure with Decision Trees}}",
booktitle = "Proceedings of IEEE ICDM",
year="2010",
pages={334--343},
}

@article{lowd:jmlr14,
        author = "Lowd, Daniel and Davis, Jesse",
        journal = "The Journal of Machine Learning Research",
        number = "1",
        pages = "501–532",
        publisher = "JMLR. org",
        title = "{Improving Markov network structure learning using decision trees}",
        volume = "15",
        year = "2014"
}
@article{bordes&al09,
author={A. Bordes and Léon Bottou and P. Gallinari},
title={{SGD-QN}: Careful Quasi-{N}ewton Stochastic Gradient Descent},
volume={10},
year={2009},
pages={1737--1754},
journal=jmlr
}

@inproceedings{singla2005discriminative,
  title={Discriminative training of Markov logic networks},
  author={Singla, Parag and Domingos, Pedro},
  booktitle={AAAI},
  volume={5},
  pages={868--873},
  year={2005}
}

@article{sutton2011introduction,
  title={An introduction to conditional random fields},
  author={Sutton, Charles and McCallum, Andrew},
  journal={Machine Learning},
  volume={4},
  number={4},
  pages={267--373},
  year={2011}
}


@inproceedings{collins2002discriminative,
  title={Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms},
  author={Collins, Michael},
  booktitle={Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10},
  pages={1--8},
  year={2002},
  organization={Association for Computational Linguistics}
}

@inproceedings{huynh2008discriminative,
  title={{Discriminative Structure and Parameter Learning for Markov Logic Networks}},
  author={Huynh, T.N. and Mooney, R.J.},
  booktitle={Proceedings of the Twenty-Fifth International Conference on Machine Learning},
  pages={416--423},
  year={2008},
  organization=acm
}
@inproceedings{gogate:nips10,
  author    = {V. Gogate and
               W.A. Webb and
               P. Domingos},
  title     = {Learning Efficient Markov Networks},
  booktitle = {Advances in Neural Information Processing Systems 23},
  year      = {2010},
  pages     = {748-756},

}
@inproceedings{lowd&rooshenas13,
  title={Learning {M}arkov Networks with Arithmetic Circuits},
  author={Lowd, D. and Rooshenas, A.},
  booktitle={Proc.\ AISTATS},
  year={2013},
  pages = {406--414},
}

@article{ChowLiu,
    author = "C. K. Chow and C. N. Liu",
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    journal = "IEEE Transactions on Information Theory",
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    pages = "462-467",
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@comment{x-kbibtex-personnameformatting=<%l><, %f>}

@article{JacquetLagreze1982151,
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        doi = "10.1016/0377-2217(82)90155-2",
        issn = "0377-2217",
        journal = "European Journal of Operational Research",
        note = "",
        number = "2",
        pages = "151--164",
        title = "{Assessing a set of additive utility functions for multicriteria decision-making, the \{UTA\} method}",
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        year = "1982"
}

@inproceedings{bees,
  title={Building for environmental and economic sustainability (BEES)},
  author={Lippiatt, Barbara},
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  year={1998}
}

@inproceedings{pang2004sentimental,
  title={A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts},
  author={Pang, Bo and Lee, Lillian},
  booktitle={Proceedings of ACL},
  pages={271},
  year={2004},
}

@article{porter1980algorithm,
  title={An algorithm for suffix stripping},
  author={Porter, Martin F},
  journal={Program},
  volume={14},
  number={3},
  pages={130--137},
  year={1980},
  publisher={MCB UP Ltd}
}

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={JMLR},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@article{niepert2012markov,
  title={Markov chains on orbits of permutation groups},
  author={Niepert, Mathias},
  journal={Proceedings of UAI},
  year={2012}
}

@article{niepert2013symmetry,
  title={Symmetry-aware marginal density estimation},
  author={Niepert, Mathias},
  journal={Proceedings of AAAI},
  year={2013}
}


@inproceedings{niepert2014exchangeable,
  title={Exchangeable variable models},
  author={Niepert, M. and Domingos, P.},
  booktitle={Proceedings of ICML},
  year={2014}
}

@article{zhang2004hierarchical,
  title={Hierarchical latent class models for cluster analysis},
  author={Zhang, N. L.},
  journal={Journal of Machine Learning Research},
  volume={5},
  pages={697--723},
  year={2004},
}

@inproceedings{rooshenas2014learning,
  title={Learning sum-product networks with direct and indirect variable interactions},
  author={Rooshenas, A. and Lowd, D.},
  booktitle={Proceedings ICML},
  pages={710--718},
  year={2014}
}

@inproceedings{gens2013learning,
  title={Learning the structure of sum-product networks},
  author={Gens, R. and Domingos, P.},
  booktitle={Proceedings of ICLM},
  pages={873--880},
  year={2013}
}

@incollection{peharz2013greedy,
  title={Greedy part-wise learning of sum-product networks},
  author={Peharz, Robert and Geiger, Bernhard C and Pernkopf, Franz},
  booktitle={Machine Learning and Knowledge Discovery in Databases},
  pages={612--627},
  year={2013},
  publisher={Springer}
}

@article{kulesza2012determinantal,
  title={Determinantal point processes for machine learning},
  author={Kulesza, A. and Taskar, B.},
  journal={Foundations and Trends in Machine Learning},
  year={2012}
}

@article{maua2013complexity,
  title={On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables},
  author={Mau{\'a}, D. D. and De Campos, C. P. and Zaffalon, M.},
  journal={AIJ},
  volume={205},
  pages={30--38},
  year={2013},
  publisher={Elsevier}
}

@inproceedings{park2002solving,
  title={Solving MAP exactly using systematic search},
  author={Park, James D and Darwiche, Adnan},
  booktitle={Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence},
  pages={459--468},
  year={2002},
  organization={Morgan Kaufmann Publishers Inc.}
}

@inproceedings{park2002map,
  title={MAP complexity results and approximation methods},
  author={Park, J. D.},
  booktitle={Proceedings of UAI},
  year={2002},
}

@inproceedings{park2001approximating,
  title={Approximating MAP using local search},
  author={Park, James D and Darwiche, Adnan},
  booktitle={Proceedings of the Seventeenth conference on Uncertainty in Artificial Intelligence},
  pages={403--410},
  year={2001},
  organization={Morgan Kaufmann Publishers Inc.}
}

@inproceedings{van2004monotonicity,
  title={Monotonicity in Bayesian networks},
  author={van der Gaag, L. C. and Bodlaender, H. L. and Feelders, A.},
  booktitle={Proceedings of UAI},
  pages={569--576},
  year={2004},
}

@article{chen2014algorithms,
  title={Algorithms and applications for the same-decision probability},
  author={Chen, S. J. and Choi, A. and Darwiche, A.},
  journal={JAIR},
  pages={601--633},
  year={2014}
}

@inproceedings{krause05optimal,
        Author = {Krause, A.and Guestrin, C.},
        Booktitle = {Proceedings of IJCAI},
        Title = {Optimal Nonmyopic Value of Information in Graphical Models - Efficient Algorithms and Theoretical Limits},
        Year = {2005}}
     
@book{wegener2000branching,
  title={Branching programs and binary decision diagrams: theory and applications},
  author={Wegener, I.},
  year={2000},
  publisher={SIAM}
}

@article{parberry1989relating,
  title={Relating {Boltzmann} machines to conventional models of computation},
  author={Parberry, I. and Schnitger, G.},
  journal={Neural Networks},
  volume={2},
  number={1},
  pages={59--67},
  year={1989},
}


@inproceedings{Kersting:2012,
        author = "Kersting, Kristian",
        booktitle = "{Proceedings of the 20th European Conference on Artificial Intelligence}",
        title = "{Lifted Probabilistic Inference}",
        year = "2012"
}

@inproceedings{buchman2015representing,
  title={Representing Aggregators in Relational Probabilistic Models},
  author={Buchman, D. and Poole, D.},
  Booktitle = {Proceedings of AAAI},
  year={2015}
}

@inproceedings{Poole2003,
        author = "Poole, David",
        booktitle = "{Proceedings of the 18th International Joint Conference on Artificial Intelligence}",
        pages = "985--991",
        title = "{First-Order Probabilistic Inference}",
        year = "2003"
}


@inproceedings{pipatsrisawat2010top,
  title={Top-Down Algorithms for Constructing Structured {DNNF}: Theoretical and Practical Implications.},
  author={Pipatsrisawat, K. and Darwiche, A.},
  booktitle={Proceedings of ECAI},
  pages={3--8},
  year={2010}
}

@inproceedings{gomes2006near,
  title={Near-uniform sampling of combinatorial spaces using XOR constraints},
  author={Gomes, Carla P and Sabharwal, Ashish and Selman, Bart},
  booktitle={Advances In Neural Information Processing Systems},
  pages={481--488},
  year={2006}
}

@inproceedings{bui2012exact,
  title={{Exact Lifted Inference with Distinct Soft Evidence on Every Object}},
  author={Bui, Hung B and Huynh, Tuyen N and de Salvo Braz, Rodrigo},
        booktitle = "{Proceedings of the 26th AAAI Conference on Artificial Intelligence}",
  year={2012}
}  

@article{MengshoelCCPDU10,
  author    = {Ole J. Mengshoel and
               Mark Chavira and
               Keith Cascio and
               Scott Poll and
               Adnan Darwiche and
               N. Serdar Uckun},
  title     = {Probabilistic Model-Based Diagnosis: An Electrical Power System Case
               Study},
  journal   = {{IEEE} Transactions on Systems, Man, and Cybernetics, Part {A}},
  volume    = {40},
  number    = {5},
  pages     = {874--885},
  year      = {2010},
}

@inproceedings{ChoiKisaDarwiche13,
author = {Choi, A. and Kisa, D. and Darwiche, A.},
title = {Compiling Probabilistic Graphical Models using Sentential Decision Diagrams},
booktitle = {ECSQARU},
year = {2013},
pages = {121-132},
}

@article{LowdDavis14,
 author = {Lowd, D. and Davis, J.},
 title = {Improving Markov Network Structure Learning Using Decision Trees},
 journal = {J. Mach. Learn. Res.},
 issue_date = {January 2014},
 volume = {15},
 number = {1},
 month = jan,
 year = {2014},
 issn = {1532-4435},
 pages = {501--532},
 numpages = {32},
 myurl = {http://dl.acm.org/citation.cfm?id=2627435.2627451},
 acmid = {2627451},
 publisher = {JMLR.org},
 keywords = {Markov networks, decision trees, probabilistic methods, structure learning},
} 

@inproceedings{choiPsdd15,
  title={Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions},
  author={Choi, A. and Van den Broeck, G. and Darwiche, A.},
  booktitle={Proceedings of IJCAI},
  year={2015}
}

@article{nowlan1992simplifying,
  title={Simplifying neural networks by soft weight-sharing},
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  number={4},
  pages={473--493},
  year={1992},
  publisher={MIT Press}
}

@article{korzen2013logistic,
  title={Logistic regression with weight grouping priors},
  author={Korze{\'n}, M and Jaroszewicz, S and Klesk, P},
  journal={Computational Statistics \& Data Analysis},
  volume={64},
  pages={281--298},
  year={2013},
  publisher={Elsevier}
}

@inproceedings{van2014tractable2,
  title={Tractable learning of liftable {M}arkov logic networks},
  author={Van Haaren, J. and Van den Broeck, G. and Meert, W. and Davis, J.},
  booktitle={Proceedings of the ICML-14 Workshop on LTPM},
  year={2014}
}

@inproceedings{lptm,
          author="P. Domingos and M. Niepert and D. Lowd (Eds.)",
          booktitle={ICML Workshop on Learning Tractable Probabilistic Models},
          year="2014"
          } 

@inproceedings{OztokD15,
author = {Umut Oztok and Adnan Darwiche},
title = {A Top-Down Compiler for Sentential Decision Diagrams},
booktitle = {Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI)},
year = {2015},
}

@inproceedings{OztokD14b,
author = {Umut Oztok and Adnan Darwiche},
title = {On Compiling CNF into Decision-DNNF},
booktitle = {Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming (CP)},
pages = {42--57},
year = {2014},
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year = {2015},
notes = {To appear}
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Vaishak Belle at 2015-04-29 13:52:05 +0200 


%% Saved with string encoding Unicode (UTF-8) 

@inproceedings{VdBKRR15,
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  title     = {Towards High-Level Probabilistic Reasoning with Lifted Inference},
  booktitle = {Proceedings of KRR},
  year      = {2015},
  myurl = {http://web.cs.ucla.edu/~guyvdb/papers/VdBKRR15.pdf},
  keywords   = {workshop,selected}
}

@inproceedings{BelleUAI15,
  author    = {Vaishak Belle and Guy {Van den Broeck} and Andrea Passerini},
  title     = {Hashing-Based Approximate Probabilistic Inference in Hybrid Domains},
  booktitle= {UAI},
  year = {2015},
  myhideurl       ={http://web.cs.ucla.edu/~guyvdb/papers/BelleUAI15.pdf},
  annotation = "(UAI best paper award)",
  keywords   = {conference,strong,selected}
}

@inproceedings{DBLP:conf/icml/ErmonGSS14,
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  year      = {2014},
  crossref  = {DBLP:conf/icml/2014},
  myhideurl       = {http://jmlr.org/proceedings/papers/v32/ermon14.html},
  timestamp = {Thu, 11 Sep 2014 07:28:55 +0200},
  bibmyurl    = {http://dblp.uni-trier.de/rec/bib/conf/icml/ErmonGSS14},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@proceedings{DBLP:conf/icml/2014,
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@article{journals/siamcomp/DyerF88,
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  year = 1988
}



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@article{RenkensMLJ12,
	Author = {Renkens, Joris and {Van den Broeck}, Guy and Nijssen, Siegfried},
	Journal = {Machine Learning},
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	Month = Jul,
	Number = {3},
	Pages = {215--231},
	Title = {k-optimal: {A} novel approximate inference algorithm for {P}rob{L}og},
	Volume = {89},
	Year = {2012}}

@article{KimmigAMCJournal,
	Author = {Kimmig, Angelika and {Van den Broeck}, Guy and {De Raedt}, Luc},
	Keywords = {authored,journal},
	Note = {(submitted)},
	Title = {{Algebraic Model Counting}},
	Year = {2012}}

@inproceedings{369425,
	Author = {Fierens, Daan and Van den Broeck, Guy and Bruynooghe, Maurice and De Raedt, Luc},
	Booktitle = {Proceedings of the NIPS Probabilistic Programming Workshop,},
	Editor = {Roy, Daniel and Mansinghka, Vikash and Goodman, Noah},
	Keywords = {authored,workshop},
	Month = Dec,
	Title = {Constraints for probabilistic logic programming},
	Url = {https://lirias.kuleuven.be/handle/123456789/369425},
	Year = {2012},
	Bdsk-Url-1 = {https://lirias.kuleuven.be/handle/123456789/369425}}

	@inproceedings{VlasselaerIJCAI15,
  author    = {Jonas Vlasselaer and Guy {Van den Broeck} and Angelika Kimmig and Wannes Meert and Luc {De Raedt}},
  title     = {Anytime Inference in Probabilistic Logic Programs with {Tp}-compilation},
  booktitle = {Proceedings of IJCAI},
  year      = {2015},
  myurl = {http://web.cs.ucla.edu/~guyvdb/papers/VlasselaerIJCAI15.pdf},
  keywords   = {conference,strong,selected}
}
	
@inproceedings{369419,
	Author = {Meert, Wannes and Van den Broeck, Guy and Taghipour, Nima and Fierens, Daan and Blockeel, Hendrik and Davis, Jesse and De Raedt, Luc},
	Booktitle = {Proceedings of the NIPS Probabilistic Programming Workshop,},
	Keywords = {authored,workshop},
	Month = Dec,
	Title = {Lifted inference for probabilistic programming},
	Url = {https://lirias.kuleuven.be/handle/123456789/369419},
	Year = {2012},
	Bdsk-Url-1 = {https://lirias.kuleuven.be/handle/123456789/369419}}

@inproceedings{369427,
	Author = {Renkens, Joris and Shterionov, Dimitar and Van den Broeck, Guy and Vlasselaer, Jonas and Fierens, Daan and Meert, Wannes and Janssens, Gerda and De Raedt, Luc},
	Booktitle = {Proceedings of the NIPS Probabilistic Programming Workshop,},
	Editor = {Roy, Daniel and Mansinghka, Vikash and Goodman, Noah},
	Keywords = {authored,workshop},
	Month = Dec,
	Note = {Accepted},
	Title = {Prob{L}og2: {F}rom probabilistic programming to statistical relational learning},
	Url = {https://lirias.kuleuven.be/handle/123456789/369427},
	Year = {2012},
	Bdsk-Url-1 = {https://lirias.kuleuven.be/handle/123456789/369427}}

@misc{VdBAIStats13,
	Author = {{Van den Broeck}, Guy and Meert, Wannes and Davis, Jesse},
	Keywords = {authored,conference,strongselection},
	Note = {(submitted)},
	Title = {Lifted parameter learning for {Markov} logic},
	Year = {2012}}

@inproceedings{JaegerStarAI12,
	Author = {Jaeger, Manfred and {Van den Broeck}, Guy},
	Booktitle = {Proceedings of the 2nd International Workshop on Statistical Relational AI,},
	Keywords = {authored,workshop},
	Title = {Liftability of probabilistic inference: {U}pper and lower bounds},
	Year = {2012}}

@inproceedings{VdBUAI12,
	Author = {{Van den Broeck}, Guy and Choi, Arthur and Darwiche, Adnan},
	Booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI)},
	Editor = {de Freitas, Nando and Murphy, Kevin},
	Keywords = {authored,conference,strongselection},
	Title = {Lifted relax, compensate and then recover: {F}rom approximate to exact lifted probabilistic inference},
	Year = {2012}}

@inproceedings{VdBAAAI12,
	Author = {{Van den Broeck}, Guy and Davis, Jesse},
	Booktitle = {Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence,},
	Editor = {Hoffmann, Joerg and Selman, Bart},
	Keywords = {authored,conference,strongselection},
	Location = {Palo Alto, California, USA},
	Publisher = {AAAI Press},
	Title = {Conditioning in first-order knowledge compilation and lifted probabilistic inference},
	Year = {2012}}


@inproceedings{KimmigAAAI11,
	Author = {Kimmig, Angelika and {Van den Broeck}, Guy and De Raedt, Luc},
	Booktitle = {Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence,},
	Editor = {Burgard, Wolfram and Roth, Dan},
	Keywords = {authored,conference,strongselection},
	Pages = {209--214},
	Publisher = {AAAI Press},
	Title = {An algebraic {P}rolog for reasoning about possible worlds},
	Year = {2011}}

@inproceedings{NittiILP11,
	Author = {Nitti, Davide and {Van den Broeck}, Guy and De Raedt, Luc},
	Booktitle = {Preliminary Papers ILP 2011,},
	Keywords = {authored,conference,weakselection},
	Pages = {1--6},
	Title = {Probabilistic logic in dynamic domains: {P}article filter with distributional clauses},
	Year = {2011}}

@inproceedings{RenkensILP11,
	Author = {Renkens, Joris and {Van den Broeck}, Guy and Nijssen, Siegfried},
	Booktitle = {Preliminary Papers ILP 2011,},
	Keywords = {authored,conference,weakselection},
	Pages = {1--6},
	Title = {k-{O}ptimal: {A} novel approximate inference algorithm for {P}rob{L}og},
	Year = {2011}}

@inproceedings{VdBNIPS11,
	Author = {{Van den Broeck}, Guy},
	Booktitle = {Advances in Neural Information Processing Systems 24 (NIPS),},
	Keywords = {authored,conference,strongselection},
	Pages = {1386--1394},
	Title = {On the completeness of first-order knowledge compilation for lifted probabilistic inference},
	Year = {2011}}

@inproceedings{VdBMLDMG11,
	Author = {{Van den Broeck}, Guy and Driessens, Kurt},
	Booktitle = {Proceedings of the ECML/PKDD 2011 Workshop on Machine Learning and Data Mining in and around Games,},
	Editor = {Croonenborghs, Tom and Driessens, Kurt and Missura, Olana},
	Keywords = {authored,workshop},
	Pages = {1--12},
	Title = {Automatic discretization of actions and states in {M}onte-{C}arlo tree search},
	Year = {2011}}

@inproceedings{VdBIJCAI11,
	Author = {{Van den Broeck}, Guy and Taghipour, Nima and Meert, Wannes and Davis, Jesse and De Raedt, Luc},
	Booktitle = {Proceedings of IJCAI},
	Pages = {2178--2185},
	Title = {Lifted probabilistic inference by first-order knowledge compilation},
	Year = {2011}}

@inproceedings{HaarenILP11,
	Author = {Van Haaren, Jan and {Van den Broeck}, Guy},
	Booktitle = {Preliminary Papers ILP 2011,},
	Keywords = {authored,conference,weakselection},
	Pages = {1--6},
	Title = {Relational learning for football-related predictions},
	Year = {2011}}

	
@inproceedings{VdBAAAI10,
	Author = {{Van den Broeck}, Guy and Thon, Ingo and van Otterlo, Martijn and De Raedt, Luc},
	Booktitle = {Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence,},
	Editor = {Fox, Maria and Poole, David},
	Keywords = {authored,conference,strongselection},
	Location = {Menlo Park, California},
	Pages = {1217--1222},
	Publisher = {AAAI Press},
	Title = {{DTP}rob{L}og: {A} decision-theoretic probabilistic {P}rolog},
	Year = {2010}}

@inproceedings{BruynoogheSRL09,
	Author = {Bruynooghe, Maurice and De Cat, Broes and Drijkoningen, Jochen and Fierens, Daan and Goos, Jan and Gutmann, Bernd and Kimmig, Angelika and Labeeuw, Wouter and Langenaken, Steven and Landwehr, Niels and Meert, Wannes and Nuyts, Ewoud and Pellegrims, Robin and Rymenants, Roel and Segers, Stefan and Thon, Ingo and Van Eyck, Jelle and {Van den Broeck}, Guy and Vangansewinkel, Tine and Van Hove, Lucie and Vennekens, Joost and Weytjens, Timmy and De Raedt, Luc},
	Booktitle = {International Workshop on Statistical Relational Learning, Leuven, Belgium, 2-4 July 2009},
	Editor = {Domingos, Pedro and Kersting, Kristian},
	Keywords = {authored,workshop},
	Title = {An exercise with statistical relational learning systems},
	Year = {2009}}

@inproceedings{VdBLNCS09,
	Author = {{Van den Broeck}, Guy and Driessens, Kurt and Ramon, Jan},
	Booktitle = {Proceedings of the 1st Asian Conference on Machine Learning (ACML), Lecture Notes in Computer Science},
	Doi = {10.1007/978-3-642-05224-8},
	Keywords = {authored,conference,strongselection},
	Pages = {367--381},
	Publisher = {Springer},
	Title = {Monte-{C}arlo tree search in poker using expected reward distributions},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-642-05224-8}}

@unpublished{346058,
	Author = {Blockeel, Hendrik and Davis, Jesse and De Raedt, Luc and Fierens, Daan and Meert, Wannes and Taghipour, Nima and {Van den Broeck}, Guy},
	Keywords = {authored,abstract},
	Note = {Spring Workshop on Mining and Learning, Bad Neuenahr, Germany, 18-20 April 2012},
	Title = {Recent advances in lifted inference at {L}euven},
	Url = {https://lirias.kuleuven.be/handle/123456789/346058},
	Year = {2012},
	Bdsk-Url-1 = {https://lirias.kuleuven.be/handle/123456789/346058}}

@unpublished{345606,
	Author = {Van Haaren, Jan and {Van den Broeck}, Guy},
	Booktitle = {Proceedings of the 21st Belgian-Dutch Conference on Machine Learning},
	Keywords = {authored,abstract},
	Note = {BeneLearn, Gent, Belgium, 24-25 May 2012},
	Pages = {85},
	Title = {Relational learning for football-related predictions},
	Year = {2012}}

@unpublished{317976,
	Author = {{Van den Broeck}, Guy},
	Keywords = {authored,abstract},
	Note = {SIKS Symposium on Strategic Decision-Making in Complex Games, Maastricht, the Netherlands, 15 June 2011},
	Title = {Monte-{C}arlo tree search for multi-player, no-limit {T}exas hold'em poker},
	Year = {2011}}

@unpublished{317055,
	Author = {{Van den Broeck}, Guy and Taghipour, Nima and Meert, Wannes and Davis, Jesse and De Raedt, Luc},
	Keywords = {authored,abstract},
	Note = {IJCAI Tutorial on Lifted Inference in Probabilistic Logical Models, Barcelona, Spain, 18 July 2011},
	Title = {Lifted probabilistic inference by first-order knowledge compilation},
	Year = {2011}}

@unpublished{ThonStarAI10,
	Author = {Thon, Ingo and Gutmann, Bernd and {Van den Broeck}, Guy},
	Editor = {Kersting, Kristian and Russell, Stuart and Pack Kaelbling, Leslie and Halevy, Alon and Natarajan, Sriraam and Mihalkova, Lilyana},
	Keywords = {authored,workshop},
	Note = {Statistical Relational AI workshop, Atlanta, USA, 12. July 2010},
	Title = {Probabilistic programming for planning problems},
	Year = {2010}}

@unpublished{250970,
	Author = {{Van den Broeck}, Guy and Driessens, Kurt and Ramon, Jan},
	Booktitle = {Proceedings of the 21st Benelux Conference on Artificial Intelligence},
	Editor = {Calders, Toon and Tuyls, Karl and Pechenizkiy, Mykola},
	Keywords = {authored,abstract},
	Note = {Benelux Conference on Artificial Intelligence (BNAIC), Eindhoven, the Netherlands, 29-30 October 2009},
	Title = {Monte-{C}arlo tree search in poker using expected reward distributions},
	Year = {2009}}

@techreport{311251,
	Author = {Fierens, Daan and {Van den Broeck}, Guy and Thon, Ingo and Gutmann, Bernd and De Raedt, Luc},
	Institution = {Department of Computer Science, K.U.Leuven},
	Keywords = {authored,techreport},
	Location = {Leuven, Belgium},
	Number = {CW607},
	Title = {Inference in probabilistic logic programs using weighted {CNF}'s},
	Type = {CW Reports},
	Year = {2011}}

@misc{311832,
	Author = {Kimmig, Angelika and Gutmann, Bernd and Mantadelis, Theofrastos and {Van den Broeck}, Guy and Santos Costa, Vitor and Janssens, Gerda and De Raedt, Luc},
	Keywords = {authored,abstract},
	Note = {ALP Newsletter},
	Pages = {3},
	Publisher = {Association for Logic Programming},
	Title = {Prob{L}og},
	Year = {2011}}

@misc{317054,
	Author = {{Van den Broeck}, Guy},
	Keywords = {authored,abstract},
	Note = {BeScala Meet-up, 13 September 2011},
	Title = {Probabilistic programming in {S}cala},
	Year = {2011}}

@article{NimaAIStats12,
	Author = {{Taghipour}, N. and {Fierens}, D. and {Van den Broeck}, G. and {Davis}, J. and {Blockeel}, H.},
	Keywords = {authored,conference,strongselection},
	Note = {(submitted)},
	Title = {Completeness Results for Lifted Variable Elimination},
	Year = 2012}

@article{NimaArxiv12,
	Archiveprefix = {arXiv},
	Author = {{Taghipour}, N. and {Fierens}, D. and {Van den Broeck}, G. and {Davis}, J. and {Blockeel}, H.},
	Eprint = {1208.3809},
	Journal = {ArXiv e-prints},
	Keywords = {authored,techreport},
	Month = aug,
	Primaryclass = {cs.AI},
	Title = {Lifted Variable Elimination: A Novel Operator and Completeness Results},
	Year = 2012}

@article{KimmigAMC,
	Archiveprefix = {arXiv},
	Author = {Kimmig, Angelika and {Van den Broeck}, Guy and {De Raedt}, Luc},
	Citeulike-Article-Id = {11732157},
	Citeulike-Linkout-0 = {http://arxiv.org/abs/1211.4475},
	Citeulike-Linkout-1 = {http://arxiv.org/pdf/1211.4475},
	Day = {19},
	Eprint = {1211.4475},
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@inproceedings{10.1145/3192366.3192408,
author = {Wang, Di and Hoffmann, Jan and Reps, Thomas},
title = {PMAF: An Algebraic Framework for Static Analysis of Probabilistic Programs},
year = {2018},
isbn = {9781450356985},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3192366.3192408},
doi = {10.1145/3192366.3192408},
abstract = {Automatically establishing that a probabilistic program satisfies some property ϕ is a challenging problem. While a sampling-based approach—which involves running the program repeatedly—can suggest that ϕ holds, to establish that the program satisfies ϕ, analysis techniques must be used. Despite recent successes, probabilistic static analyses are still more difficult to design and implement than their deterministic counterparts. This paper presents a framework, called PMAF, for designing, implementing, and proving the correctness of static analyses of probabilistic programs with challenging features such as recursion, unstructured control-flow, divergence, nondeterminism, and continuous distributions. PMAF introduces pre-Markov algebras to factor out common parts of different analyses. To perform interprocedural analysis and to create procedure summaries, PMAF extends ideas from non-probabilistic interprocedural dataflow analysis to the probabilistic setting. One novelty is that PMAF is based on a semantics formulated in terms of a control-flow hyper-graph for each procedure, rather than a standard control-flow graph. To evaluate its effectiveness, PMAF has been used to reformulate and implement existing intraprocedural analyses for Bayesian-inference and the Markov decision problem, by creating corresponding interprocedural analyses. Additionally, PMAF has been used to implement a new interprocedural linear expectation-invariant analysis. Experiments with benchmark programs for the three analyses demonstrate that the approach is practical.},
booktitle = {Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation},
pages = {513–528},
numpages = {16},
keywords = {probabilistic programming, pre-Markov algebra, expectation invariant, Program analysis},
location = {Philadelphia, PA, USA},
series = {PLDI 2018}
}


@article{wang2018pmaf,
author = {Wang, Di and Hoffmann, Jan and Reps, Thomas},
title = {PMAF: An Algebraic Framework for Static Analysis of Probabilistic Programs},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {53},
number = {4},
issn = {0362-1340},
url = {https://doi.org/10.1145/3296979.3192408},
doi = {10.1145/3296979.3192408},
abstract = {Automatically establishing that a probabilistic program satisfies some property ϕ is a challenging problem. While a sampling-based approach—which involves running the program repeatedly—can suggest that ϕ holds, to establish that the program satisfies ϕ, analysis techniques must be used. Despite recent successes, probabilistic static analyses are still more difficult to design and implement than their deterministic counterparts. This paper presents a framework, called PMAF, for designing, implementing, and proving the correctness of static analyses of probabilistic programs with challenging features such as recursion, unstructured control-flow, divergence, nondeterminism, and continuous distributions. PMAF introduces pre-Markov algebras to factor out common parts of different analyses. To perform interprocedural analysis and to create procedure summaries, PMAF extends ideas from non-probabilistic interprocedural dataflow analysis to the probabilistic setting. One novelty is that PMAF is based on a semantics formulated in terms of a control-flow hyper-graph for each procedure, rather than a standard control-flow graph. To evaluate its effectiveness, PMAF has been used to reformulate and implement existing intraprocedural analyses for Bayesian-inference and the Markov decision problem, by creating corresponding interprocedural analyses. Additionally, PMAF has been used to implement a new interprocedural linear expectation-invariant analysis. Experiments with benchmark programs for the three analyses demonstrate that the approach is practical.},
journal = {SIGPLAN Not.},
month = jun,
pages = {513–528},
numpages = {16},
keywords = {probabilistic programming, Program analysis, pre-Markov algebra, expectation invariant}
}


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@misc{https://doi.org/10.48550/arxiv.1802.06312,
  doi = {10.48550/ARXIV.1802.06312},
  
  url = {https://arxiv.org/abs/1802.06312},
  
  author = {Dittmer, Samuel and Pak, Igor},
  
  keywords = {Combinatorics (math.CO), FOS: Mathematics, FOS: Mathematics},
  
  title = {Counting linear extensions of restricted posets},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@article{doi:10.1080/00029890.1938.11990797,
author = {D. H. Lehmer},
title = {Euclid's Algorithm for Large Numbers},
journal = {The American Mathematical Monthly},
volume = {45},
number = {4},
pages = {227-233},
year  = {1938},
publisher = {Taylor & Francis},
doi = {10.1080/00029890.1938.11990797},

URL = { 
    
        https://doi.org/10.1080/00029890.1938.11990797
    
    

},
eprint = { 
    
        https://doi.org/10.1080/00029890.1938.11990797
    
    

}

}


@InProceedings{10.1007/978-3-030-44914-8_14,
author="Laurel, Jacob
and Misailovic, Sasa",
editor="M{\"u}ller, Peter",
title="Continualization of Probabilistic Programs With Correction",
booktitle="Programming Languages and Systems",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="366--393",
abstract="Probabilistic Programming offers a concise way to represent stochastic models and perform automated statistical inference. However, many real-world models have discrete or hybrid discrete-continuous distributions, for which existing tools may suffer non-trivial limitations. Inference and parameter estimation can be exceedingly slow for these models because many inference algorithms compute results faster (or exclusively) when the distributions being inferred are continuous. To address this discrepancy, this paper presents Leios. Leios is the first approach for systematically approximating arbitrary probabilistic programs that have discrete, or hybrid discrete-continuous random variables. The approximate programs have all their variables fully continualized. We show that once we have the fully continuous approximate program, we can perform inference and parameter estimation faster by exploiting the existing support that many languages offer for continuous distributions. Furthermore, we show that the estimates obtained when performing inference and parameter estimation on the continuous approximation are still comparably close to both the true parameter values and the estimates obtained when performing inference on the original model.",
isbn="978-3-030-44914-8"
}

@InProceedings{10.1007/978-3-642-21295-6_12,
author="Gutmann, Bernd
and Jaeger, Manfred
and De Raedt, Luc",
editor="Frasconi, Paolo
and Lisi, Francesca A.",
title="Extending ProbLog with Continuous Distributions",
booktitle="Inductive Logic Programming",
year="2011",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="76--91",
abstract="ProbLog is a recently introduced probabilistic extension of Prolog. The key contribution of this paper is that we extend ProbLog with abilities to specify continuous distributions and that we show how ProbLog's exact inference mechanism can be modified to cope with such distributions. The resulting inference engine combines an interval calculus with a dynamic discretization algorithm into an effective solver.",
isbn="978-3-642-21295-6"
}

@article{staton,
  doi = {10.4230/LIPICS.ICALP.2018.141},
  
  hideurl = {http://drops.dagstuhl.de/opus/volltexte/2018/9145/},
  
  author = {Staton, Sam and Stein, Dario and Yang, Hongseok and Ackerman, Nathanael L. and Freer, Cameron E. and Roy, Daniel M.},
  
  keywords = {Computer Science, 000 Computer science, knowledge, general works},
  
  language = {en},
  
  title = {The Beta-Bernoulli process and algebraic effects},
  
  publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany},
  
  year = {2018}
}


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  number={5},
  pages={530--543},
  year={2015},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}

@article{stuhlmuller2014reasoning,
  title={Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs},
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  pages={80--99},
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@article{van2018introduction,
  title={An introduction to probabilistic programming},
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  year={2018}
}

@misc{https://doi.org/10.48550/arxiv.1711.10604,
  doi = {10.48550/ARXIV.1711.10604},
  
  url = {https://arxiv.org/abs/1711.10604},
  
  author = {Dillon, Joshua V. and Langmore, Ian and Tran, Dustin and Brevdo, Eugene and Vasudevan, Srinivas and Moore, Dave and Patton, Brian and Alemi, Alex and Hoffman, Matt and Saurous, Rif A.},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Programming Languages (cs.PL), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {TensorFlow Distributions},
  
  publisher = {arXiv},
  
  year = {2017},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@misc{https://doi.org/10.48550/arxiv.1701.02434,
  doi = {10.48550/ARXIV.1701.02434},
  
  url = {https://arxiv.org/abs/1701.02434},
  
  author = {Betancourt, Michael},
  
  keywords = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {A Conceptual Introduction to Hamiltonian Monte Carlo},
  
  publisher = {arXiv},
  
  year = {2017},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{HoltzenOOPSLA20,
  author = {Holtzen, Steven and {Van den Broeck}, Guy and Millstein, Todd},
  title = {Scaling Exact Inference for Discrete Probabilistic Programs},
  year = {2020},
  pages = {140:1--140:31},
  numpages = {31},
  booktitle={Proc. ACM Program. Lang.},
  series = {OOPSLA 2020},
  publisher = {Association for Computing Machinery},
  doi = {10.1145/3428208}
}


@inproceedings{pfanschilling2022sum,
  title={Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming},
  author={Pfanschilling, Viktor and Shindo, Hikaru and Dhami, Devendra Singh and Kersting, Kristian},
  booktitle={Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning},
  volume={19},
  number={1},
  pages={453--462},
  year={2022}
}