Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks

DOI10.1016/j.ijar.2006.07.020zbMath1116.68080OpenAlexW2016619340WikidataQ57551191 ScholiaQ57551191MaRDI QIDQ881799

N. E. Zubov

Publication date: 18 May 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.2006.07.020

zbMATH Keywords

Bayesian networksprobability intervalsbranch-and-bound algorithmsstrong independencecredal networkhill-climbing

Mathematics Subject Classification ID

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

Related Items (7)

Updating credal networks is approximable in polynomial time ⋮ Learning recursive probability trees from probabilistic potentials ⋮ Approximate algorithms for credal networks with binary variables ⋮ Thirty years of credal networks: specification, algorithms and complexity ⋮ Unifying parameter learning and modelling complex systems with epistemic uncertainty using probability interval ⋮ Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks ⋮ Approximate credal network updating by linear programming with applications to decision making

Cites Work

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• Interval probability propagation
• Using probability trees to compute marginals with imprecise probabilities
• Credal networks
• Strong conditional independence for credal sets
• Graphical models for imprecise probabilities
• Computing probability intervals with simulated annealing and probability trees
• On Information and Sufficiency

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