Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks

DOI10.1016/S0004-3702(96)00022-7zbMath0906.68116OpenAlexW1987051875MaRDI QIDQ1391897

D. Massart

Publication date: 23 July 1998

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0004-3702(96)00022-7

zbMATH Keywords

Bayesian networksprobabilistic inferencemodel-based diagnosisconflicts

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies and applications (68U99)

Related Items (5)

Exploiting case-based independence for approximating marginal probabilities ⋮ Dynamic importance sampling in Bayesian networks based on probability trees ⋮ Anytime anyspace probabilistic inference ⋮ The Independent Choice Logic and Beyond ⋮ Complexity of probabilistic reasoning in directed-path singly-connected Bayes networks

Cites Work

• Fusion, propagation, and structuring in belief networks
• Approximating probabilistic inference in Bayesian belief networks is NP- hard
• Logic programming, abduction and probability. A top-down anytime algorithm for estimating prior and posterior probabilities
• Depth-first iterative-deepening: An optimal admissible tree search
• Evidential reasoning using stochastic simulation of causal models
• Diagnosing multiple faults
• A theory of diagnosis from first principles
• Characterizing diagnoses and systems
• Probabilistic Horn abduction and Bayesian networks
• Gibbs sampling in Bayesian networks
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