We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Bo...
David A. McAllester, Michael Collins, Fernando Per...
Abstract. This paper describes symbolic techniques for the construction, representation and analysis of large, probabilistic systems. Symbolic approaches derive their efficiency by...
Abstract--Model checkers for concurrent probabilistic systems have become very popular within the last decade. The study of long-run average behavior has however received only scan...
Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function ...
Robert Mateescu, Rina Dechter, Radu Marinescu 0002
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...