Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms

DOI10.2200/S00529ED1V01Y201308AIM023zbMath1297.68006OpenAlexW2080430334MaRDI QIDQ5416752

Rina Dechter

Publication date: 15 May 2014

Published in: Synthesis Lectures on Artificial Intelligence and Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2200/s00529ed1v01y201308aim023

zbMATH Keywords

Bayesian networksknowledge representationconditioningreasoninginferenceconstraint networksgraphical modelsvariable eliminationtree-widthMarkov networksAND/OR searchpseudo-treebucket eliminationcycle-cutsetinduced width

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Pattern recognition, speech recognition (68T10) Knowledge representation (68T30) Research exposition (monographs, survey articles) pertaining to computer science (68-02) Graphical methods in statistics (62A09)

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A new probabilistic constraint logic programming language based on a generalised distribution semantics ⋮ Computing partial hypergraphs of bounded width ⋮ Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs ⋮ Subproblem ordering heuristics for AND/OR best-first search

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