An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network

📝 Original Info

• Title: An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network
• ArXiv ID: 1304.1141
• Date: 2013-04-05
• Authors: Researchers from original ArXiv paper

📝 Abstract

We analyzed the convergence properties of likelihood- weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine. Specifically, on two difficult diagnostic cases, we examined the effects of Markov blanket scoring, importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.

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Deep Dive into An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network.

We analyzed the convergence properties of likelihood- weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine. Specifically, on two difficult diagnostic cases, we examined the effects of Markov blanket scoring, importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.

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