AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian network where the conditional distributions of the model belong to convex sets. The computation on a general Bayesian network with convex sets of conditional distributions is formalized as a global optimization problem. It is shown that such a problem can be reduced to a combinatorial problem, suitable to exact algorithmic solutions. An exact propagation algorithm for the updating of a polytree with binary variables is derived. The overall complexity is linear to the size of the network, when the maximum number of parents is fixed
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
Abstract — The traditional message passing algorithm devel-oped by Pearl in 1980s provides exact inf...
We present new polynomial time algorithms for inference problems in Bayesian networks (BNs) when res...
This paper describes a general scheme for accomodating different types of conditional distributions ...
AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A...
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in ...
This paper deals with the following problem: modify a Bayesian network to satisfy a given set of pro...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...
In many applications one wants to compute conditional probabilities given a Bayesian network. This i...
AbstractThis paper presents a search algorithm for estimating posterior probabilities in discrete Ba...
This article describes a propagation scheme for Bayesian networks with conditional Gaussian distribu...
The general problem of computing posterior probabilities in Bayesian networds is NP-hard (Cooper 199...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
We consider the problem of learning a Bayesian network structure given n examples and the prior prob...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
Abstract — The traditional message passing algorithm devel-oped by Pearl in 1980s provides exact inf...
We present new polynomial time algorithms for inference problems in Bayesian networks (BNs) when res...
This paper describes a general scheme for accomodating different types of conditional distributions ...
AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A...
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in ...
This paper deals with the following problem: modify a Bayesian network to satisfy a given set of pro...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...
In many applications one wants to compute conditional probabilities given a Bayesian network. This i...
AbstractThis paper presents a search algorithm for estimating posterior probabilities in discrete Ba...
This article describes a propagation scheme for Bayesian networks with conditional Gaussian distribu...
The general problem of computing posterior probabilities in Bayesian networds is NP-hard (Cooper 199...
Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous va...
We consider the problem of learning a Bayesian network structure given n examples and the prior prob...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
Abstract — The traditional message passing algorithm devel-oped by Pearl in 1980s provides exact inf...
We present new polynomial time algorithms for inference problems in Bayesian networks (BNs) when res...