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@misc{ nyc2023bus ,
author = {  DOE },
year = 2023 ,
title = { Bus Breakdown and Delays },
institution = { Department of Education },
type = {} ,
number = { } ,
note = {\ url { https://data.cityofnewyork.us/Transportation/Bus-Breakdown-and-Delays/ez4e-fazm } Accessed on: 05.05.2023}
}
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  title={Importance sampling-based estimation over and/or search spaces for graphical models},
  author={Gogate, Vibhav and Dechter, Rina},
  journal={Artificial Intelligence},
  volume={184},
  pages={38--77},
  year={2012},
  publisher={Elsevier}
}

@article{dechter2018abstraction,
  title={Abstraction Sampling in Graphical Models},
  author={Dechter, Rina and Broka, Filjor and Kask, Kalev and Ihler, Alexander},
  journal={AAAI},
  year={2018}
}

@article{charlier2021keops,
  author  = {Benjamin Charlier and Jean Feydy and Joan Alexis Glaunès and François-David Collin and Ghislain Durif},
  title   = {Kernel Operations on the GPU, with Autodiff, without Memory Overflows},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
}

@misc{jax2018github,
  author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url = {http://github.com/google/jax},
  version = {0.3.13},
  year = {2018},
}

@article{homan_no-u-turn_2014,
	title = {The {No}-{U}-{Turn} {Sampler}: {Adaptively} {Setting} {Path} {Lengths} in {Hamiltonian} {Monte} {Carlo}},
	abstract = {Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by ﬁrst-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC’s performance is highly sensitive to two user-speciﬁed parameters: a step size and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS performs at least as eﬃciently as (and sometimes more eﬃciently than) a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter on the ﬂy based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all, making it suitable for applications such as BUGS-style automatic inference engines that require eﬃcient “turnkey” samplers.},
	language = {en},
	author = {Hoﬀman, Matthew D and Gelman, Andrew},
	month = apr,
	year = {2014},
	file = {Hoﬀman and Gelman - The No-U-Turn Sampler Adaptively Setting Path Len.pdf:/home/dg22309/Zotero/storage/SDRXWDY7/Hoﬀman and Gelman - The No-U-Turn Sampler Adaptively Setting Path Len.pdf:application/pdf},
}

@article{salvatier_probabilistic_2016,
	title = {Probabilistic programming in {Python} using {PyMC3}},
	volume = {2},
	issn = {2376-5992},
	url = {https://peerj.com/articles/cs-55},
	doi = {10.7717/peerj-cs.55},
	abstract = {Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.},
	language = {en},
	urldate = {2023-05-13},
	journal = {PeerJ Computer Science},
	author = {Salvatier, John and Wiecki, Thomas V. and Fonnesbeck, Christopher},
	month = apr,
	year = {2016},
	note = {Publisher: PeerJ Inc.},
	pages = {e55},
	file = {Full Text PDF:/home/dg22309/Zotero/storage/WYU5TFAQ/Salvatier et al. - 2016 - Probabilistic programming in Python using PyMC3.pdf:application/pdf},
}

@article{hoffman2014no,
  title={The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.},
  author={Hoffman, Matthew D and Gelman, Andrew and others},
  journal={J. Mach. Learn. Res.},
  volume={15},
  number={1},
  pages={1593--1623},
  year={2014}
}
@article{doser2022spoccupancy,
  title={spOccupancy: An R package for single-species, multi-species, and integrated spatial occupancy models},
  author={Doser, Jeffrey W and Finley, Andrew O and K{\'e}ry, Marc and Zipkin, Elise F},
  journal={Methods in Ecology and Evolution},
  volume={13},
  number={8},
  pages={1670--1678},
  year={2022},
  publisher={Wiley Online Library}
}
@article{bbs2022,
  title={The North American breeding bird survey, analysis results 1966--2021},
  author={Ziolkowski Jr., D.J., Lutmerding, M., Aponte, V.I., and Hudson, M-A.R.},
  journal={US Geological Survey data release},
  year={2022},
  doi={https://doi.org/10.5066/P97WAZE5}
}