Title: Probabilistic sentential decision diagrams: Learning with massive logical constraints
Authors: Kisa, Doga
Van den Broeck, Guy
Choi, Arthur
Darwiche, Adnan
Issue Date: Jun-2014
Host Document: pages:1-9
Conference: ICML Workshop on Learning Tractable Probabilistic Models (LTPM) edition:1 location:Beijing, China date:June 2014
Article number: 8
Abstract: We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical representation of probability distributions defined over the models of a given propositional theory. Each parameter of a PSDD can be viewed as the (conditional) probability of making a decision in a corresponding Sentential Decision Diagram (SDD). The SDD itself is a recently proposed complete and canonical representation of propositional theories. We explore a number of interesting properties of PSDDs, including the independencies that underlie them. We show that the PSDD is a tractable representation. We further show how the parameters of a PSDD can be efficiently estimated, in closed form, from complete data. We empirically evaluate the quality of PSDDs learned from data, when we have knowledge, a priori, of the domain logical constraints.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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