Hybrid random fields. A scalable approach to structure and parameter learning in probabilistic graphical models

DOI10.1007/978-3-642-20308-4zbMath1278.60004OpenAlexW2501347266MaRDI QIDQ535474

Antonino Freno, Edmondo Trentin

Publication date: 12 May 2011

Published in: Intelligent Systems Reference Library (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-20308-4

zbMATH Keywords

hypothesis testingMarkov processesapplicationsgraph theoryrandom fieldsBayesian inferenceprobability theorylearning and adaptive systemsresearch exposition

Mathematics Subject Classification ID

Random fields (60G60) Bayesian inference (62F15) Applications of graph theory (05C90) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Markov processes: hypothesis testing (62M02) Research exposition (monographs, survey articles) pertaining to probability theory (60-02)

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