an:05216959
Zbl 1161.68784
McAllester, David; Collins, Michael; Pereira, Fernando
Case-factor diagrams for structured probabilistic modeling
EN
J. Comput. Syst. Sci. 74, No. 1, 84-96 (2008).
00213591
2008
j
68T20 60C05 68Q42
Boolean decision diagrams; Markov random fields; probabilistic context free grammars; hidden Markov models; conditional random fields; probabilistic relational models; structured labeling; graphical models; Bayesian networks; zero supression; recursive conditioning; and/or graphs
Summary: We introduce a probabilistic formalism handling both Markov random fields of bounded tree width and probabilistic context-free grammars. Our models are based on Case-Factor Diagrams (CFDs) which are similar to binary decision diagrams but are more concise for circuits of bounded tree width. A probabilistic model consists of a CFD defining a feasible set of Boolean assignments and a weight (or cost) for each individual Boolean variable. We give versions of the inside-outside algorithm and the Viterbi algorithm for these models.