Hybrid algorithms for approximate belief updating in Bayes nets

DOI10.1016/S0888-613X(97)00012-1zbMath0939.68119WikidataQ57518796 ScholiaQ57518796MaRDI QIDQ1809372

Yanyan Li

Publication date: 20 December 1999

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

zbMATH Keywords

randomized algorithmsBayes nets

Mathematics Subject Classification ID

Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (6)

Estimating mixtures of truncated exponentials in hybrid Bayesian networks ⋮ Dynamic importance sampling in Bayesian networks based on probability trees ⋮ A framework for building knowledge-bases under uncertainty ⋮ Approximate probability propagation with mixtures of truncated exponentials ⋮ DIRECTING GENETIC ALGORITHMS FOR PROBABILISTIC REASONING THROUGH REINFORCEMENT LEARNING ⋮ Lazy evaluation in penniless propagation over join trees

Cites Work

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• Approximating probabilistic inference in Bayesian belief networks is NP- hard
• Exploiting case-based independence for approximating marginal probabilities
• Probabilistic Horn abduction and Bayesian networks
• Finding MAPs for belief networks is NP-hard
• The role of relevance in explanation. I: Irrelevance as statistical independence
• The computational complexity of probabilistic inference using Bayesian belief networks

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