Theoretical analysis and practical insights on importance sampling in Bayesian networks

DOI10.1016/j.ijar.2006.09.006zbMath1133.68456OpenAlexW2010548278MaRDI QIDQ2463642

Changhe Yuan, Marek J. Druzdzel

Publication date: 14 December 2007

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2006.09.006

zbMATH Keywords

importance sampling-based algorithm

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (1)

Arc refractor methods for adaptive importance sampling on large Bayesian networks under evidential reasoning

Uses Software

• AIS-BN

Cites Work

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• Approximating probabilistic inference in Bayesian belief networks is NP- hard
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• A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques
• Importance sampling algorithms for Bayesian networks: principles and performance
• The computational complexity of probabilistic inference using Bayesian belief networks
• Dynamically Weighted Importance Sampling in Monte Carlo Computation
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
• Monte Carlo strategies in scientific computing

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