This paper combines two classic results from two different fields: the result by Lauritzen and Spiegelhalter [21] that the probabilistic inference problem on probabilistic networks can be solved in linear time on networks with a moralization of bounded treewidth, and the result by Courcelle [10] that problems that can be formulated in counting monadic second order logic can be solved in linear time on graphs of bounded treewidth. It is shown that, given a probabilistic network whose moralization has bounded treewidth and a property P of the network and the values of the variables that can be formulated in counting monadic second order logic, one can determine in linear time the probability that P holds.</p
This article describes the basic ideas and algorithms behind specification and inference in probabil...
Probabilistic Logic and Probabilistic Networks presents a groundbreaking framework within which vari...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...
This paper combines two classic results from two different fields: the result by Lauritzen and Spieg...
International audienceThis paper combines two classic results from two different fields: the ...
AbstractThe method of conditioning permits probabilistic inference in multiply connected belief netw...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...
We study the problem of reasoning in the probabilistic De-scription Logic BEL. Using a novel structu...
We present new polynomial time algorithms for inference problems in Bayesian networks (BNs) when res...
Udgivelsesdato: JANWe adopt probabilistic decision graphs developed in the field of automated verifi...
We adopt probabilistic decision graphs developed in the field of automated verification as a tool fo...
In this thesis, the computational complexity of a number of problems related to probabilistic networ...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic...
This article describes the basic ideas and algorithms behind specification and inference in probabil...
Probabilistic Logic and Probabilistic Networks presents a groundbreaking framework within which vari...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...
This paper combines two classic results from two different fields: the result by Lauritzen and Spieg...
International audienceThis paper combines two classic results from two different fields: the ...
AbstractThe method of conditioning permits probabilistic inference in multiply connected belief netw...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...
We study the problem of reasoning in the probabilistic De-scription Logic BEL. Using a novel structu...
We present new polynomial time algorithms for inference problems in Bayesian networks (BNs) when res...
Udgivelsesdato: JANWe adopt probabilistic decision graphs developed in the field of automated verifi...
We adopt probabilistic decision graphs developed in the field of automated verification as a tool fo...
In this thesis, the computational complexity of a number of problems related to probabilistic networ...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic...
This article describes the basic ideas and algorithms behind specification and inference in probabil...
Probabilistic Logic and Probabilistic Networks presents a groundbreaking framework within which vari...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...