This paper describes a scheme for local computation in conditional Gaussian Bayesian networks that combines the approach of Lauritzen and Jensen (2001) with some elements of Shachter and Kenley (1989). Message passing takes place on an elimination tree structure rather than the more compact (and usual) junction tree of cliques. This yields a local computation scheme in which all calculations involving the continuous variables are performed by manipulating univariate regressions, and hence matrix operations are avoided
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
This paper considers conditional Gaussian networks. The parameters in the network are learned by usi...
Given a Bayesian network relative to a set I of discrete random variables, we are interested in comp...
This article describes a propagation scheme for Bayesian networks with conditional Gaussian distribu...
AbstractEver since Kim and Pearl provided an exact message-passing algorithm for updating probabilit...
AbstractLocal conditioning (LC) is an exact algorithm for computing probability in Bayesian networks...
AbstractIn recent years, Bayesian networks with a mixture of continuous and discrete variables have ...
This paper describes a general scheme for accomodating different types of conditional distributions ...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is ...
Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise natural...
Abstract—Novel lazy Lauritzen-Spiegelhalter (LS), lazy Hugin and lazy Shafer-Shenoy (SS) algorithms ...
Belief update in a Bayesian network using Lazy Propagation (LP) proceeds by message passing over a j...
The general problem of computing posterior probabilities in Bayesian networds is NP-hard (Cooper 199...
AbstractAn important class of continuous Bayesian networks are those that have linear conditionally ...
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
This paper considers conditional Gaussian networks. The parameters in the network are learned by usi...
Given a Bayesian network relative to a set I of discrete random variables, we are interested in comp...
This article describes a propagation scheme for Bayesian networks with conditional Gaussian distribu...
AbstractEver since Kim and Pearl provided an exact message-passing algorithm for updating probabilit...
AbstractLocal conditioning (LC) is an exact algorithm for computing probability in Bayesian networks...
AbstractIn recent years, Bayesian networks with a mixture of continuous and discrete variables have ...
This paper describes a general scheme for accomodating different types of conditional distributions ...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is ...
Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise natural...
Abstract—Novel lazy Lauritzen-Spiegelhalter (LS), lazy Hugin and lazy Shafer-Shenoy (SS) algorithms ...
Belief update in a Bayesian network using Lazy Propagation (LP) proceeds by message passing over a j...
The general problem of computing posterior probabilities in Bayesian networds is NP-hard (Cooper 199...
AbstractAn important class of continuous Bayesian networks are those that have linear conditionally ...
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
This paper considers conditional Gaussian networks. The parameters in the network are learned by usi...
Given a Bayesian network relative to a set I of discrete random variables, we are interested in comp...