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. 1 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. PSDDs are tractable representations, and further, 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
Graphical models provide a powerful framework for reasoning under uncertainty, and an influence diag...
AbstractProbabilistic decision graphs (PDGs) are a representation language for probability distribut...
AbstractIn this article we present the framework of Possibilistic Influence Diagrams (PID), which al...
We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical representa...
We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical representa...
The probabilistic sentential decision diagram (PSDD) was recently introduced as a tractable represen...
Probabilistic sentential decision diagrams (PSDDs) are a tractable representation of structured prob...
Probabilistic Sentential Decision Diagrams (PSDDs) have been proposed for learning tractable probabi...
Knowledge compilation algorithms transform a probabilistic logic program into a circuit representati...
Methods that learn the structure of Probabilistic Sentential Decision Diagrams (PSDD) from data have...
This paper describes symbolic techniques for the construction, representation and analysis of large,...
The paper discusses the application of Binary Decision Diagrams (BDDs) in the reconstructability ana...
International audienceIn this paper we extend one of the main tools used in verification of discrete...
This paper reports on experimental results with symbolic model checking of probabilistic processes ...
Our goal is to develop general-purpose techniques for probabilistic reasoning and learning in struct...
Graphical models provide a powerful framework for reasoning under uncertainty, and an influence diag...
AbstractProbabilistic decision graphs (PDGs) are a representation language for probability distribut...
AbstractIn this article we present the framework of Possibilistic Influence Diagrams (PID), which al...
We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical representa...
We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical representa...
The probabilistic sentential decision diagram (PSDD) was recently introduced as a tractable represen...
Probabilistic sentential decision diagrams (PSDDs) are a tractable representation of structured prob...
Probabilistic Sentential Decision Diagrams (PSDDs) have been proposed for learning tractable probabi...
Knowledge compilation algorithms transform a probabilistic logic program into a circuit representati...
Methods that learn the structure of Probabilistic Sentential Decision Diagrams (PSDD) from data have...
This paper describes symbolic techniques for the construction, representation and analysis of large,...
The paper discusses the application of Binary Decision Diagrams (BDDs) in the reconstructability ana...
International audienceIn this paper we extend one of the main tools used in verification of discrete...
This paper reports on experimental results with symbolic model checking of probabilistic processes ...
Our goal is to develop general-purpose techniques for probabilistic reasoning and learning in struct...
Graphical models provide a powerful framework for reasoning under uncertainty, and an influence diag...
AbstractProbabilistic decision graphs (PDGs) are a representation language for probability distribut...
AbstractIn this article we present the framework of Possibilistic Influence Diagrams (PID), which al...