Learning multi-linear representations of distributions for efficient inference

DOI10.1007/s10994-009-5130-xzbMath1470.68165OpenAlexW4240838124MaRDI QIDQ1959506

Dan Roth, Rajhans Samdani

Publication date: 7 October 2010

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-009-5130-x

zbMATH Keywords

probabilistic inferencegraphical modelslearning probability distributionsmulti-linear polynomials

Mathematics Subject Classification ID

Probability distributions: general theory (60E05) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Uses Software

• UCI-ml
• WinMine

Cites Work

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• Maximum likelihood bounded tree-width Markov networks
• Learning Bayesian networks: The combination of knowledge and statistical data
• Learning probabilistic decision graphs
• 10.1162/153244301753344605
• A differential approach to inference in Bayesian networks
• Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms
• Graphical Models, Exponential Families, and Variational Inference
• Recursive conditioning

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