Importance Sampling for General Hybrid Bayesian Networks

Changhe Yuan, Marek J. Druzdzel
; Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:652-659, 2007.

Abstract

Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.

Cite this Paper


BibTeX
@InProceedings{pmlr-v2-yuan07a, title = {Importance Sampling for General Hybrid Bayesian Networks}, author = {Changhe Yuan and Marek J. Druzdzel}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {652--659}, year = {2007}, editor = {Marina Meila and Xiaotong Shen}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/yuan07a/yuan07a.pdf}, url = {http://proceedings.mlr.press/v2/yuan07a.html}, abstract = {Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.} }
Endnote
%0 Conference Paper %T Importance Sampling for General Hybrid Bayesian Networks %A Changhe Yuan %A Marek J. Druzdzel %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-yuan07a %I PMLR %J Proceedings of Machine Learning Research %P 652--659 %U http://proceedings.mlr.press %V 2 %W PMLR %X Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.
RIS
TY - CPAPER TI - Importance Sampling for General Hybrid Bayesian Networks AU - Changhe Yuan AU - Marek J. Druzdzel BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics PY - 2007/03/11 DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-yuan07a PB - PMLR SP - 652 DP - PMLR EP - 659 L1 - http://proceedings.mlr.press/v2/yuan07a/yuan07a.pdf UR - http://proceedings.mlr.press/v2/yuan07a.html AB - Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time. ER -
APA
Yuan, C. & Druzdzel, M.J.. (2007). Importance Sampling for General Hybrid Bayesian Networks. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in PMLR 2:652-659

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