Improved Sampling for Diagnostic Reasoning in Bayesian Networks
Artificial Intelligence
2013-02-21 v1
Abstract
Bayesian networks offer great potential for use in automating large scale diagnostic reasoning tasks. Gibbs sampling is the main technique used to perform diagnostic reasoning in large richly interconnected Bayesian networks. Unfortunately Gibbs sampling can take an excessive time to generate a representative sample. In this paper we describe and test a number of heuristic strategies for improving sampling in noisy-or Bayesian networks. The strategies include Monte Carlo Markov chain sampling techniques other than Gibbs sampling. Emphasis is put on strategies that can be implemented in distributed systems.
Cite
@article{arxiv.1302.4961,
title = {Improved Sampling for Diagnostic Reasoning in Bayesian Networks},
author = {Mark Hulme},
journal= {arXiv preprint arXiv:1302.4961},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)