Bayesian Inference in the Presence of Determinism

David Larkin, Rina Dechter
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, PMLR R4:187-194, 2003.

Abstract

In this paper, we consider the problem of performing inference on Bayesian networks which exhibit a substantial degree of determinism. We improve upon the determinismexploiting inference algorithm presented in [4], showing that the information brought to light by constraint propagation may be exploited to a much greater extent than has been previously possible. This is confirmed with theoretical and empirical studies.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR4-larkin03a, title = {Bayesian Inference in the Presence of Determinism}, author = {Larkin, David and Dechter, Rina}, booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics}, pages = {187--194}, year = {2003}, editor = {Bishop, Christopher M. and Frey, Brendan J.}, volume = {R4}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r4/larkin03a/larkin03a.pdf}, url = {https://proceedings.mlr.press/r4/larkin03a.html}, abstract = {In this paper, we consider the problem of performing inference on Bayesian networks which exhibit a substantial degree of determinism. We improve upon the determinismexploiting inference algorithm presented in [4], showing that the information brought to light by constraint propagation may be exploited to a much greater extent than has been previously possible. This is confirmed with theoretical and empirical studies.}, note = {Reissued by PMLR on 01 April 2021.} }
Endnote
%0 Conference Paper %T Bayesian Inference in the Presence of Determinism %A David Larkin %A Rina Dechter %B Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2003 %E Christopher M. Bishop %E Brendan J. Frey %F pmlr-vR4-larkin03a %I PMLR %P 187--194 %U https://proceedings.mlr.press/r4/larkin03a.html %V R4 %X In this paper, we consider the problem of performing inference on Bayesian networks which exhibit a substantial degree of determinism. We improve upon the determinismexploiting inference algorithm presented in [4], showing that the information brought to light by constraint propagation may be exploited to a much greater extent than has been previously possible. This is confirmed with theoretical and empirical studies. %Z Reissued by PMLR on 01 April 2021.
APA
Larkin, D. & Dechter, R.. (2003). Bayesian Inference in the Presence of Determinism. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R4:187-194 Available from https://proceedings.mlr.press/r4/larkin03a.html. Reissued by PMLR on 01 April 2021.

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