acceptance rate 26%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 ev...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Representing uncertain information is crucial for modeling real world domains. This has been fully r...
AbstractThe paper presents the proof-theoretical approach to a probabilistic logic which allows expr...
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...
Abstract We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical r...
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...
Knowledge compilation algorithms transform a probabilistic logic program into a circuit representati...
Probabilistic Sentential Decision Diagrams (PSDDs) have been proposed for learning tractable probabi...
Our goal is to develop general-purpose techniques for probabilistic reasoning and learning in struct...
Many domains, such as health care, gain benefit from machine learning if a certain degree of accurac...
Methods that learn the structure of Probabilistic Sentential Decision Diagrams (PSDD) from data have...
Tractable learning aims to learn probabilistic models where inference is guaran-teed to be efficient...
Abstract. Representing uncertain information is crucial for modeling real world domains. In this pap...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Representing uncertain information is crucial for modeling real world domains. This has been fully r...
AbstractThe paper presents the proof-theoretical approach to a probabilistic logic which allows expr...
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...
Abstract We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and canonical r...
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...
Knowledge compilation algorithms transform a probabilistic logic program into a circuit representati...
Probabilistic Sentential Decision Diagrams (PSDDs) have been proposed for learning tractable probabi...
Our goal is to develop general-purpose techniques for probabilistic reasoning and learning in struct...
Many domains, such as health care, gain benefit from machine learning if a certain degree of accurac...
Methods that learn the structure of Probabilistic Sentential Decision Diagrams (PSDD) from data have...
Tractable learning aims to learn probabilistic models where inference is guaran-teed to be efficient...
Abstract. Representing uncertain information is crucial for modeling real world domains. In this pap...
We present a semantics for Probabilistic Description Logics that is based on the distribution semant...
Representing uncertain information is crucial for modeling real world domains. This has been fully r...
AbstractThe paper presents the proof-theoretical approach to a probabilistic logic which allows expr...