A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques

DOI10.1016/S0888-613X(97)10004-4zbMath0941.68155MaRDI QIDQ1817998

Antonio Salmerón, Luis D. Hernández, Serafín Moral

Publication date: 27 January 2000

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

zbMATH Keywords

Monte Carlo algorithmsbelief networks

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (6)

Theoretical analysis and practical insights on importance sampling in Bayesian networks ⋮ Importance sampling algorithms for Bayesian networks: principles and performance ⋮ Dynamic importance sampling in Bayesian networks based on probability trees ⋮ Arc refractor methods for adaptive importance sampling on large Bayesian networks under evidential reasoning ⋮ Importance sampling in Bayesian networks using probability trees. ⋮ Lazy evaluation in penniless propagation over join trees

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• Probabilistic Inference and Influence Diagrams
• An Accurate Approximation to the Sampling Distribution of the Studentized Extreme Value Statistic
• Mixing exact and importance sampling propagation algorithms in dependence graphs

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