The belief network is a well-known graphi­ cal structure for representing independences in a joint probability distribution. The meth­ ods, which perform probabilistic inference in belief networks, often treat the conditional probabilities which are stored in the network as certain values. However, if one takes ei­ ther a subjectivistic or a limiting frequency approach to probability, one can n...

An efficient framework is proposed for the fast recovery of Bayesian network classifier. A novel algorithm, called Iterative Parent-Child learningBayesian Network Classifier (IPC-BNC), is proposed to learn a BNC without having to learn the complete Bayesian network first. IPC-BNC was proved correct and more efficient compared with a traditional global learning algorithm, called PC, by requiring...

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We suggest Darwinian networks (DNs) as a simplification of working with Bayesian networks (BNs). DNs adapt a handful of wellknown concepts in biology into a single framework that is surprisingly simple, yet remarkably robust. With respect to modeling, on one hand, DNs not only represent BNs, but also faithfully represent the testing of independencies in a more straightforward fashion. On the ot...

This study confirmed the ability of the Dempster–Shafer theory (DST) and the Dezert–Smarandache (Free DSm model) theory to significantly improve the quality of maps of regenerating forest stands in southern Quebec, Canada compared to a classical Maximum Likelihood Algorithm (MLA). The proposed approach uses data fusion methods that allow the integration of remotely sensed imagery with conventio...

Bayesian networks are commonly used for classification: a structural learning algorithm determines the network graph, while standard approaches estimate the model parameters from data. Yet, with few data the corresponding assessments can be unreliable. To gain robustness in this phase, we consider a likelihood-based learning approach, which takes all the model quantifications whose likelihood e...

When a complex information system is modelled by a Bayesian network the backward inference is normal requirement in system management. This paper proposes one inference algorithm in Bayesian networks, which can track the strongest causes and trace the strongest routes between particular effects and their causes. This proposed algorithm will become the foundation for further intelligent decision...

In high dimensional data, it is often very difficult to analytically evaluate the likelihood function, and thus hard to get a Bayesian posterior estimation. Approximate Bayesian Computation is an important algorithm in this application. However, to apply the algorithm, we need to compress the data into low dimensional summary statistics, which is typically hard to get in an analytical form. In ...

This doctoral dissertation introduces an algorithm for constructing the most probable Bayesian network from data for small domains. The algorithm is used to show that a popular goodness criterion for the Bayesian networks has a severe sensitivity problem. The dissertation then proposes an information theoretic criterion that avoids the problem. Computing Reviews (1998)