Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams

DOI10.1016/j.ijar.2015.03.007zbMath1346.68203OpenAlexW2174277655WikidataQ62046522 ScholiaQ62046522MaRDI QIDQ895536

Denis Deratani Mauá

Publication date: 3 December 2015

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

Full work available at URL: https://doi.org/10.1016/j.ijar.2015.03.007

zbMATH Keywords

computational complexityBayesian networksinfluence diagramsmaximum expected utilitymaximum a posteriori inference

Mathematics Subject Classification ID

Multivariate analysis (62H99) Reasoning under uncertainty in the context of artificial intelligence (68T37) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17)

Related Items (1)

An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes

Uses Software

• k-Neighborhood

Cites Work

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• Probabilistic Inference in Credal Networks: New Complexity Results
• A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth

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