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      Improved Sampling for Diagnostic Reasoning in Bayesian Networks

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          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.

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          Author and article information

          Journal
          2013-02-20
          Article
          1302.4961
          a705d474-e91a-4300-b108-336f5a5ed795

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          UAI-P-1995-PG-315-322
          Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
          cs.AI
          auai

          Artificial intelligence
          Artificial intelligence

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