Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present

DOI10.1007/978-3-319-11433-0_31zbMath1443.68156OpenAlexW1608177285MaRDI QIDQ2938425

Joe Suzuki

Publication date: 14 January 2015

Published in: Probabilistic Graphical Models (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-11433-0_31

zbMATH Keywords

universalityKullback-Leibler divergencedensity estimationcontinuous variablesdiscrete variableslearning Bayesian network structures

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Statistical aspects of information-theoretic topics (62B10) Probabilistic graphical models (62H22)

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