This article describes a propagation scheme for Bayesian networks with conditional Gaussian distributions that does not have the numerical weaknesses of the scheme derived in Lauritzen (Journal of the American Statistical Association 87: 1098-1108, 1992). The propagation architecture is that of Lauritzen and Spiegelhalter (Journal of the Royal Statistical Society, Series B 50: 157-224, 1988). In addition to the means and variances provided by the previous algorithm, the new propagation scheme yields full local marginal distributions. The new scheme also handles linear deterministic relationships between continuous variables in the network specification. The computations involved in the new propagation scheme are simpler than those in the pr...
AbstractLocal conditioning (LC) is an exact algorithm for computing probability in Bayesian networks...
An important class of hybrid Bayesian networks are those that have conditionally de-terministic vari...
In this paper we show how discrete and continuous variables can be combined using parametric conditi...
This paper describes a scheme for local computation in conditional Gaussian Bayesian networks that c...
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 ...
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
AbstractAn important class of continuous Bayesian networks are those that have linear conditionally ...
AbstractEver since Kim and Pearl provided an exact message-passing algorithm for updating probabilit...
This paper considers conditional Gaussian networks. The parameters in the network are learned by usi...
Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is ...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
Abstract—Novel lazy Lauritzen-Spiegelhalter (LS), lazy Hugin and lazy Shafer-Shenoy (SS) algorithms ...
AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian...
An important class of continuous Bayesian networks are those that have linear conditionally determin...
AbstractLocal conditioning (LC) is an exact algorithm for computing probability in Bayesian networks...
An important class of hybrid Bayesian networks are those that have conditionally de-terministic vari...
In this paper we show how discrete and continuous variables can be combined using parametric conditi...
This paper describes a scheme for local computation in conditional Gaussian Bayesian networks that c...
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 ...
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
AbstractAn important class of continuous Bayesian networks are those that have linear conditionally ...
AbstractEver since Kim and Pearl provided an exact message-passing algorithm for updating probabilit...
This paper considers conditional Gaussian networks. The parameters in the network are learned by usi...
Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is ...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
Abstract—Novel lazy Lauritzen-Spiegelhalter (LS), lazy Hugin and lazy Shafer-Shenoy (SS) algorithms ...
AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian...
An important class of continuous Bayesian networks are those that have linear conditionally determin...
AbstractLocal conditioning (LC) is an exact algorithm for computing probability in Bayesian networks...
An important class of hybrid Bayesian networks are those that have conditionally de-terministic vari...
In this paper we show how discrete and continuous variables can be combined using parametric conditi...