Solving Influence Diagrams Using Gibbs Sampling

Ali Jenzarli
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:278-284, 1995.

Abstract

We describe a Monte Carlo method for solving influence diagrams. This method is a combination of stochastic dynamic programming and Gibbs sampling, an iterative Markov chain Monte Carlo algorithm. Our method is especially useful when exact methods for solving influence diagrams fail.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-jenzarli95a, title = {Solving Influence Diagrams Using Gibbs Sampling}, author = {Jenzarli, Ali}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {278--284}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/jenzarli95a/jenzarli95a.pdf}, url = {https://proceedings.mlr.press/r0/jenzarli95a.html}, abstract = {We describe a Monte Carlo method for solving influence diagrams. This method is a combination of stochastic dynamic programming and Gibbs sampling, an iterative Markov chain Monte Carlo algorithm. Our method is especially useful when exact methods for solving influence diagrams fail.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Solving Influence Diagrams Using Gibbs Sampling %A Ali Jenzarli %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-jenzarli95a %I PMLR %P 278--284 %U https://proceedings.mlr.press/r0/jenzarli95a.html %V R0 %X We describe a Monte Carlo method for solving influence diagrams. This method is a combination of stochastic dynamic programming and Gibbs sampling, an iterative Markov chain Monte Carlo algorithm. Our method is especially useful when exact methods for solving influence diagrams fail. %Z Reissued by PMLR on 01 May 2022.
APA
Jenzarli, A.. (1995). Solving Influence Diagrams Using Gibbs Sampling. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:278-284 Available from https://proceedings.mlr.press/r0/jenzarli95a.html. Reissued by PMLR on 01 May 2022.

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