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@InProceedings{Monti19UAI,
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@article{JSSv076i02,
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@inproceedings{Kingma2014,
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@article{lingam,
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@INPROCEEDINGS{Himberg01,
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@InProceedings{Kaban,
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@article{fastica,
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@article{nguyen10,  
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@article{Burgess2018,
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@article{higgins_beta-vae_2016,
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@phdthesis{Kingma2017,
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@misc{weng2018VAE,
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url = {https://doi.org/10.1162/neco.1995.7.6.1129},
doi = {10.1162/neco.1995.7.6.1129},
journal = {Neural Comput.},
month = nov,
pages = {1129-1159},
numpages = {31}
},

@INPROCEEDINGS{Cardoso01,
    author = {Jean-Francois Cardoso},
    title = {The Three Easy Routes To Independent Component Analysis, Contrasts And Geometry},
    booktitle = {Proc. ICA 2001},
    year = {2001},
    pages = {1--6}
},

@article{darmois,
 ISSN = {03731138},
OPTURL = {http://www.jstor.org/stable/1401511},
 author = {G. Darmois},
 journal = {Revue de l'Institut International de Statistique / Review of the International Statistical Institute},
 number = {1/2},
 pages = {2--8},
 publisher = {[International Statistical Institute (ISI), Wiley]},
 title = {Analyse g\'{e}n\'{e}rale des liaisons stochastiques: etude particuli\`{e}re de l'analyse factorielle lin\'{e}aire},
 volume = {21},
 year = {1953}
},

@book{brent2013algorithms,
  title={Algorithms for minimization without derivatives},
  author={Brent, Richard P},
  year={2013},
  publisher={Courier Corporation}
}



@ARTICLE{eeg,  author={Vigario, R. and Sarela, J. and Jousmiki, V. and Hamalainen, M. and Oja, E.},  journal={IEEE Transactions on Biomedical Engineering},   title="{Independent Component Approach to the Analysis of EEG and MEG Recordings}",   year={2000},  volume={47},  number={5},  pages={589-593},  doi={10.1109/10.841330}},


@InProceedings{pmlr-v97-creager19a, 
title = {Flexibly Fair Representation Learning by Disentanglement}, 
author = {Creager, Elliot and Madras, David and Jacobsen, Joern-Henrik and Weis, Marissa and Swersky, Kevin and Pitassi, Toniann and Zemel, Richard}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, 
pages = {1436--1445}, 
year = {2019}, 
editor = {Kamalika Chaudhuri and Ruslan Salakhutdinov},
volume = {97}, 
series = {Proceedings of Machine Learning Research}, 
address = {Long Beach, California, USA}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/creager19a/creager19a.pdf},OPTURL = {http://proceedings.mlr.press/v97/creager19a.html}},

@inproceedings{Khemakhem2019,
archivePrefix = {arXiv},
arxivId = {1907.04809},
author = {Khemakhem, Ilyes and Kingma, Diederik P. and Monti, Ricardo Pio and Hyv{\"{a}}rinen, Aapo},
title = {{Variational Autoencoders and Nonlinear ICA: A Unifying Framework}},
OPTurl = {http://arxiv.org/abs/1907.04809},
year = {2020},
booktitle={The 23rd International Conference on Artificial Intelligence and Statistics}
},

@article{lingam,
author={Shohei Shimizu and Patrik O. Hoyer and Aapo Hyv\"{a}rinen and Antti Kerminen}, 
title={{A Linear Non-Gaussian Acyclic Model for Causal Discovery}},
journal={Journal of Machine Learning Research},
volume={7}, 
year={2006},
OPTurl={http://www.jmlr.org/papers/volume7/shimizu06a/shimizu06a.pdf}},

@inproceedings{kaban06,
author = {Kab\'{a}n, Ata and Bingham, Ella},
title = {{ICA-Based Binary Feature Construction}},
year = {2006},
OPTisbn = {3540326308},
OPTpublisher = {Springer-Verlag},
OPTaddress = {Berlin, Heidelberg},
OPTurl = {https://doi.org/10.1007/11679363_18},
OPTdoi = {10.1007/11679363_18},
OPTabstract = {We address the problem of interactive feature construction and denoising of binary data. To this end, we develop a variational ICA method, employing a multivariate Bernoulli likelihood and independent Beta source densities. We relate this to other binary data models, demonstrating its advantages in two application domains.},
booktitle = {Proceedings of the 6th International Conference on Independent Component Analysis and Blind Signal Separation},
pages = {140-148},
numpages = {9},
OPTlocation = {Charleston, SC},
OPTseries = {ICA'06}
},

@article{Burgess2018,
archivePrefix = {arXiv},
arxivId = {1804.03599},
author = {Burgess, Christopher P. and Higgins, Irina and Pal, Arka and Matthey, Loic and Watters, Nick and Desjardins, Guillaume and Lerchner, Alexander},
eprint = {1804.03599},
journal = {Conference on Neural Information Processing Systems, NeurIPS},
title = {{Understanding disentangling in $\beta$-VAE}},
url = {http://arxiv.org/abs/1804.03599},
year = {2018}
}

@article{higgins_beta-vae_2016,
	title = {Beta-{VAE}: {Learning} {Basic} {Visual} {Concepts} with a {Constrained} {Variational} {Framework}},
	shorttitle = {beta-{VAE}},
	url = {https://openreview.net/forum?id=Sy2fzU9gl},
	author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
	year = {2016},
	journal = {International Conference on Learning Representations, ICLR},
}

@article{Chen2018,
archivePrefix = {arXiv},
arxivId = {1802.04942},
author = {Chen, Tian Qi and Li, Xuechen and Grosse, Roger and Duvenaud, David},
eprint = {1802.04942},
journal = {International Conference on Learning Representations, ICLR 2018, Workshop Track Proceedings},
number = {NeurIPS},
title = {{Isolating sources of disentanglement in variational autoencoders}},
year = {2018}
}

@phdthesis{Kingma2017,
abstract = {In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.},
author = {Kingma, Diederik P.},
title = {{Variational Inference {\&} Deep Learning: A New Synthesis}},
year = {2017}
}

@article{Doersch2016,
archivePrefix = {arXiv},
arxivId = {1606.05908},
author = {Doersch, Carl},
eprint = {1606.05908},
title = {{Tutorial on Variational Autoencoders}},
url = {http://arxiv.org/abs/1606.05908},
year = {2016}
}

@article{VanDenOord2017,
archivePrefix = {arXiv},
arxivId = {1711.00937},
author = {{Van Den Oord}, Aaron and Vinyals, Oriol and Kavukcuoglu, Koray},
eprint = {1711.00937},
title = {{Neural Discrete Representation Learning}},
year = {2017},
journal = {Conference on Neural Information Processing Systems, NeurIPS},
}

@misc{weng2018VAE,
  title   = "{From Autoencoder to Beta-VAE}",
  author  = "Weng, Lilian",
  journal = "lilianweng.github.io/lil-log",
  year    = "2018",
 OPTURL     = "http://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html"
}


@inproceedings{goodfellow_generative_2014,
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	urldate = {2018-06-12},
	booktitle = {Conference on Neural Information Processing Systems},
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	year = {2014},
},

@inproceedings{Kingma2015,
archivePrefix = {arXiv},
arxivId = {1412.6980},
author = {Kingma, Diederik P. and Ba, Jimmy Lei},
booktitle = {International Conference on Learning Representations, ICLR},
title = {{Adam: A method for stochastic optimization}},
year = {2015}
},

@techreport{Barratt,
archivePrefix = {arXiv},
arxivId = {1801.01973v2},
author = {Barratt, Shane and Sharma, Rishi},
eprint = {1801.01973v2},
title = {{A Note on the Inception Score}},
url = {https://github.com/}
},

@article{ridgeway18,
title = "{Learning Deep Disentangled Embeddings With the F-Statistic Loss}",
author = {Ridgeway, Karl and Mozer, Michael C},
year = {2018},
url = {http://papers.nips.cc/paper/7303-learning-deep-disentangled-embeddings-with-the-f-statistic-loss.pdf},
journal = {Conference on Neural Information Processing Systems, NeurIPS},
},

@article{bengio2013representation,
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  year = 2013,
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}
},

@article{imagenet_cvpr09,
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        TITLE = {{ImageNet: A Large-Scale Hierarchical Image Database}},
        journal = {IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
        YEAR = {2009},
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@article{pixelcnn,
  author    = {A{\"{a}}ron van den Oord and
               Nal Kalchbrenner and
               Oriol Vinyals and
               Lasse Espeholt and
               Alex Graves and
               Koray Kavukcuoglu},
  title     = "{Conditional Image Generation with PixelCNN Decoders}",
  journal   = {Conference on Neural Information Processing Systems, NeurIPS},
  year      = {2016},
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  archivePrefix = {arXiv},
  eprint    = {1606.05328},
  timestamp = {Mon, 13 Aug 2018 16:46:40 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/OordKVEGK16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
},

@misc{revkl,
author = {Eric Jang},
title = {A Beginner's Guide to Variational Methods: Mean-Field Approximation},
url = {https://blog.evjang.com/2016/08/variational-bayes.html},
year = {2016},
},

@article{Hinton2008ReducingTD,
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  year={2006},
  journal={Science},
},

@inproceedings{convnet,
  author = {LeCun, Yann and Bottou, Léon and Bengio, Yoshua and Haffner, Patrick},
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},

@article{higgins18,
  author    = {Irina Higgins and
               David Amos and
               David Pfau and
               S{\'{e}}bastien Racani{\`{e}}re and
               Lo{\"{\i}}c Matthey and
               Danilo J. Rezende and
               Alexander Lerchner},
  title     = {Towards a Definition of Disentangled Representations},
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  year      = {2018},
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},

@article{White16a,
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  year      = {2016},
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  archivePrefix = {arXiv},
  eprint    = {1609.04468},
},

@article{tensorflow2015-whitepaper,
title={{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={http://tensorflow.org/},
author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
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    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dan~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
  journal = {White paper},
},

@article{pytorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
journal = {Advances in Neural Information Processing Systems, NeurIPS},
year = {2019},
},

@article{lamb2016discriminative,
    title={Discriminative Regularization for Generative Models},
    author={Alex Lamb and Vincent Dumoulin and Aaron Courville},
    year={2016},
    eprint={1602.03220},
    archivePrefix={arXiv},
    primaryClass={stat.ML},
    journal={Conference on Computer Vision and Pattern Recognition, CVPR, DeepVision Workshop}
},

@article{calovae,
      author        = "Salamani, Dalila and Golling, Tobias and Gadatsch, Stefan
                       and Stewart, Graeme and Rousseau, David and Ghosh, Aishik",
      title         = "{Deep generative models for fast shower simulation in
                       ATLAS}",
      collaboration = "ATLAS Collaboration",
      year          = "2018",
      reportNumber  = "ATL-SOFT-SLIDE-2018-983",
     OPTURL           = "https://cds.cern.ch/record/2645142",
},

@article{scikit-learn,
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},

@article{Hyvarinen00independentcomponent,
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},

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author = {Gary R. Waissi and Donald F. Rossin},
},

@InProceedings{pmlr-v97-locatello19a, 
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    author = {Locatello, F. and Bauer, S. and Lucic, M. and Raetsch, G. and Gelly, S. and Sch{\"o}lkopf, B. and Bachem, O.}, 
    year = {2019}, 
    booktitle = {International Conference on Machine Learning, ICML}
},

@Book{casella,
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@misc{minfunc,
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},

@inproceedings{tcl,
 author = {Hyv\"{a}rinen, Aapo and Morioka, Hiroshi},
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 title = {{Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA}},
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},

@inproceedings{hyvarinen19,
  author = {Hyv\"{a}rinen, Aapo and Sasaki, Hiroaki and Turner, Richard E.},
  biburl = {https://www.bibsonomy.org/bibtex/22357004324e85b2d0a29c14e46452015/dblp},
  booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics},
    volume    = {89},
  publisher = {{PMLR}},
  OPTcrossref = {conf/aistats/2019},
  OPTeditor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
  OPTee = {http://proceedings.mlr.press/v89/hyvarinen19a.html},
  OPTpages = {859-868},
  OPTseries = {Proceedings of Machine Learning Research},
  title = "{Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.}",
  year = 2019
},

@InProceedings{hyvarinen17a, 
title = {{Nonlinear ICA of Temporally Dependent Stationary Sources}}, 
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pages = {460--469}, 
year = {2017}, 
volume = {54}, 
series = {Proceedings of Machine Learning Research}, 
address = {Fort Lauderdale, FL, USA}, 
month = {20--22 Apr}, 
publisher = {PMLR}, 
pdf = {http://proceedings.mlr.press/v54/hyvarinen17a/hyvarinen17a.pdf}, 
url = {http://proceedings.mlr.press/v54/hyvarinen17a.html}, 
},

@article{Hyvarinen99,
  author    = {Aapo Hyv{\"{a}}rinen and
               Petteri Pajunen},
  title     = {Nonlinear independent component analysis: Existence and uniqueness
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  biburl    = {https://dblp.org/rec/journals/nn/HyvarinenP99.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
},

@inproceedings{nice,
  author    = {Laurent Dinh and
               David Krueger and
               Yoshua Bengio},
  editor    = {Yoshua Bengio and
               Yann LeCun},
  title     = {{NICE:} Non-linear Independent Components Estimation},
  booktitle = {3rd International Conference on Learning Representations, {ICLR} 2015,
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  year      = {2015},
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  timestamp = {Wed, 17 Jul 2019 10:40:54 +0200},
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  bibsource = {dblp computer science bibliography, https://dblp.org}
},

@article{comon_94,
author = {Comon, Pierre},
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address = {USA},
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doi = {10.1016/0165-1684(94)90029-9},
journal = {Signal Process.},
month = apr,
pages = {287-314},
numpages = {28},
keywords = {array processing, mixture entropy, random variables, information, statistical independence, data analysis, principal components, blind identification, noise reduction, source separation, independent components, high-order statistics}
},

@Book{NoceWrig06,
  Title                    = {Numerical Optimization},
  Author                   = {Jorge Nocedal and Stephen J. Wright},
  Publisher                = {Springer},
  Year                     = {2006},
  Address                  = {New York, NY, USA},
  Edition                  = {second}
},

@book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
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    year={2016}
},

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doi = {10.1162/neco.1995.7.6.1129},
journal = {Neural Comput.},
month = nov,
pages = {1129-1159},
numpages = {31}
},

@INPROCEEDINGS{Cardoso01,
    author = {Jean-Francois Cardoso},
    title = {The Three Easy Routes To Independent Component Analysis, Contrasts And Geometry},
    booktitle = {Proc. ICA 2001},
    year = {2001},
    pages = {1--6}
},

@article{darmois,
 ISSN = {03731138},
OPTURL = {http://www.jstor.org/stable/1401511},
 author = {G. Darmois},
 journal = {Revue de l'Institut International de Statistique / Review of the International Statistical Institute},
 number = {1/2},
 pages = {2--8},
 publisher = {[International Statistical Institute (ISI), Wiley]},
 title = {Analyse g\'{e}n\'{e}rale des liaisons stochastiques: etude particuli\`{e}re de l'analyse factorielle lin\'{e}aire},
 volume = {21},
 year = {1953}
},

@InProceedings{nonsens, 
title = {Causal Discovery with General Non-Linear Relationships using Non-Linear ICA}, author = {Monti, Ricardo Pio and Zhang, Kun and Hyv{\"{a}}rinen, Aapo}, 
booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference}, 
pages = {186--195}, year = {2020}, 
OPTeditor = {Adams, Ryan P. and Gogate, Vibhav}, volume = {115}, 
OPTseries = {Proceedings of Machine Learning Research}, OPTmonth = {22--25 Jul}, 
publisher = {PMLR}, 
OPTpdf = {http://proceedings.mlr.press/v115/monti20a/monti20a.pdf}, 
OPTurl = { http://proceedings.mlr.press/v115/monti20a.html } }

@ARTICLE{eeg,  author={Vigario, R. and Sarela, J. and Jousmiki, V. and Hamalainen, M. and Oja, E.},  journal={IEEE Transactions on Biomedical Engineering},   title="{Independent Component Approach to the Analysis of EEG and MEG Recordings}",   year={2000},  volume={47},  number={5},  pages={589-593},  doi={10.1109/10.841330}},


@InProceedings{pmlr-v97-creager19a, 
title = {Flexibly Fair Representation Learning by Disentanglement}, 
author = {Creager, Elliot and Madras, David and Jacobsen, Joern-Henrik and Weis, Marissa and Swersky, Kevin and Pitassi, Toniann and Zemel, Richard}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, 
pages = {1436--1445}, 
year = {2019}, 
editor = {Kamalika Chaudhuri and Ruslan Salakhutdinov},
volume = {97}, 
series = {Proceedings of Machine Learning Research}, 
OPTaddress = {Long Beach, California, USA}, 
OPTmonth = {09--15 Jun}, publisher = {PMLR}, 
OPTpdf = {http://proceedings.mlr.press/v97/creager19a/creager19a.pdf}, 
OPTurl = {http://proceedings.mlr.press/v97/creager19a.html}}

@book{ICAbook,
  title={Independent Component Analysis},
  author={Hyv{\"a}rinen, Aapo and Karhunen, Juha and Oja, Erkki},
  volume={26},
  year={2001},
  publisher={John Wiley \& Sons}
}

@misc{reg,
  author = {David Olive},
  title = {Regularizing a Correlation Matrix},
  howpublished ={\url{http://parker.ad.siu.edu/Olive/slch6.pdf}},
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  note = {Accessed: 2021-10-5}
}
@misc{sigmoidtrick,
  author = {Xiaopeng Li},
  title = {Tricks of Sigmoid Function},
  howpublished ={\url{http://eelxpeng.github.io/blog/2017/03/10/Tricks-of-Sigmoid-Function}},
  year = {2021},
  note = {Accessed: 2021-10-5}
}



 @Manual{tmvtnorm,
    title = {{tmvtnorm}: Truncated Multivariate Normal and Student t
      Distribution},
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    note = {R package version 1.4-10},
    OPTurl = {http://CRAN.R-project.org/package=tmvtnorm},
  }
  
    @Book{mvnorm,
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author = {Miwa, Tetsuhisa and Hayter, A. and Kuriki, Satoshi},
year = {2003},
month = {02},
pages = {223-234},
title = {The evaluation of general non-centred orthant probabilities},
volume = {65},
journal = {Journal of the Royal Statistical Society Series B},
OPTdoi = {10.1111/1467-9868.00382}
},


@inproceedings{Podosinnikova15,
	author = {Podosinnikova, Anastasia and Bach, Francis and Lacoste-Julien, Simon},
	booktitle = {Advances in Neural Information Processing Systems},
	OPTeditor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
	publisher = {Curran Associates, Inc.},
	title = {Rethinking {L}{D}{A}: Moment Matching for Discrete {I}{C}{A}},
	OPTurl = {https://proceedings.neurips.cc/paper/2015/file/9be40cee5b0eee1462c82c6964087ff9-Paper.pdf},
	volume = {28},
	year = {2015},
	Bdsk-Url-1 = {https://proceedings.neurips.cc/paper/2015/file/9be40cee5b0eee1462c82c6964087ff9-Paper.pdf}},
	
@inproceedings{Buntine05,
author = {Buntine, Wray and Jakulin, Aleks},
title = {Discrete Component Analysis},
year = {2005},
OPTisbn = {3540341374},
publisher = {Springer-Verlag},
OPTaddress = {Berlin, Heidelberg},
OPTurl = {https://doi.org/10.1007/11752790_1},
OPTdoi = {10.1007/11752790_1},
booktitle = {Proceedings of the 2005 International Conference on Subspace, Latent Structure and Feature Selection},
OPTpages = {1–33},
OPTnumpages = {33},
OPTlocation = {Bohinj, Slovenia},
OPTseries = {SLSFS'05}
},

@inproceedings{canny04,
author = {Canny, John},
title = {{G}a{P}: A Factor Model for Discrete Data},
year = {2004},
OPTisbn = {1581138814},
publisher = {Association for Computing Machinery},
OPTaddress = {New York, NY, USA},
OPTurl = {https://doi.org/10.1145/1008992.1009016},
OPTdoi = {10.1145/1008992.1009016},
booktitle = {Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
OPTpages = {122–129},
numpages = {8},
keywords = {EM algorithm, latent semantic analysis, probabilistic models},
OPTlocation = {Sheffield, United Kingdom},
OPTseries = {SIGIR '04}
},


@InProceedings{podosinnikova16,
  title = 	 {Beyond {C}{C}{A}: Moment Matching for Multi-View Models},
  author = 	 {Podosinnikova, Anastasia and Bach, Francis and Lacoste-Julien, Simon},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {458--467},
  year = 	 {2016},
  OPTeditor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  OPTseries = 	 {Proceedings of Machine Learning Research},
  OPTaddress = 	 {New York, New York, USA},
  OPTmonth = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/podosinnikova16.pdf},
 OPTURL = 	 {https://proceedings.mlr.press/v48/podosinnikova16.html},
}

@article{genz1993comparison,
  title={Comparison of methods for the computation of multivariate normal probabilities},
  author={Genz, Alan},
  journal={Computing Science and Statistics},
  volume={25},
  pages={400--405},
  year={1993}
}