Importance sampling algorithms for the propagation of probabilities in belief networks

DOI10.1016/0888-613X(96)00013-8zbMath0949.68579WikidataQ127374569 ScholiaQ127374569MaRDI QIDQ1125700

Luis D. Hernández, Jose E. Cano, Serafín Moral

Publication date: 4 January 2000

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

zbMATH Keywords

graphical structuresimportance sampling algorithmsprobabilistic graphs

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 (4)

Uncertainty analysis for rolling contact fatigue failure probability of silicon nitride ball bearings ⋮ Imprecise Monte Carlo simulation and iterative importance sampling for the estimation of lower previsions ⋮ A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques ⋮ Importance sampling in Bayesian networks using probability trees.

Cites Work

• Approximating probabilistic inference in Bayesian belief networks is NP- hard
• Evidential reasoning using stochastic simulation of causal models
• Probability propagation
• The computational complexity of probabilistic inference using Bayesian belief networks
• Probabilistic Inference and Influence Diagrams
• An Accurate Approximation to the Sampling Distribution of the Studentized Extreme Value Statistic
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