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Discrete exponential bayesian networks structure learning for density estimation

Abstract : Our work aims at developing or expliciting bridges between Bayesian Networks and Natural Exponential Families, by proposing discrete exponential Bayesian networks as a generalization of usual discrete ones. In this paper, we illustrate the use of discrete exponential Bayesian networks for Bayesian structure learning and density estimation. Our goal is to empirically determine in which contexts these models can be a good alternative to usual Bayesian networks for density estimation.
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Contributor : Philippe Leray Connect in order to contact the contributor
Submitted on : Friday, April 17, 2020 - 6:11:28 PM
Last modification on : Wednesday, April 27, 2022 - 3:42:23 AM


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Aida Jarraya, Philippe Leray, Afif Masmoudi. Discrete exponential bayesian networks structure learning for density estimation. International Conference on Intelligent Computing, 2012, Huangshan, China. pp.?-?, ⟨10.1007/978-3-642-31837-5_21⟩. ⟨hal-00691834⟩



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