Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. A Bayesian network can thus be considered a mechanism for automatically applying Bayes’ theorem to complex problems. In the application of Bayesian networks, most of the work is related to probabilistic inferences. Any variable updating in any node of Bayesi...

In this paper, we propose an algebraic characterization for equivalent classes of Bayesian networks. Unlike the other characterizations, which are based on the graphical structure of Bayesian networks, our algebraic characterization is derived from the intrinsic algebraic structure of Bayesian networks, i.e., joint probability distribution factorization. The new proposed algebraic characterizat...

Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from mi...

In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classiier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, high-predictive-accuracy networks. We compare the performance of this classiier with selective and non-selective naive...

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In this paper, we describe how a stochastic PERT network can be formulated as a Bayesian network. We approximate such PERT Bayesian network by mixtures of Gaussians hybrid Bayesian networks. Since there exists algorithms for solving mixtures of Gaussians hybrid Bayesian networks exactly, we can use these algorithms to make inferences in PERT Bayesian networks.

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...