Bayesian networks are not easy to design and maintain. It is a time consuming process to update a Bayesian network even though only a small set of nodes with many occurrences has to be changed. In this paper, we describe a solution to these diiculties by taking an object oriented approach to constructing Bayesian networks by merging fragments of Bayesian networks. Our approach consists of a new...

Differentiation is an important inference method in Bayesian networks and intervention is a basic notion in causal Bayesian networks. In this paper, we reveal the connection between differentiation and intervention in Bayesian networks. We first encode an intervention as changing a conditional probabilistic table into a partial intervention table. We next introduce a jointree algorithm to compu...

In this paper, we describe the Hugin Tool as an efficient tool for knowledge discovery through construction of Bayesian networks by fusion of data and domain expert knowledge. The Hugin Tool supports structural learning, parameter estimation, and adaptation of parameters in Bayesian networks. The performance of the Hugin Tool is illustrated using real-world Bayesian networks, commonly used exam...

We demonstrate the application of Widening to learning performant Bayesian Networks for use as classifiers. Widening is a framework for utilizing parallel resources and diversity to find models in a hypothesis space that are potentially better than those of a standard greedy algorithm. This work demonstrates that widened learning of Bayesian Networks, using the Frobenius Norm of the networks’ g...

Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions ...

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

The complexity of probabilistic reasoning with Bayesian networks has recently been proven to be NP complete So reducing the complexity of such networks is an active issue in uncer tainty reasoning Much work on such as compressing the probabilistic information for Bayesian networks and optimal approximation algorithm for Bayesian inference has been suggested In this paper we study a class of tra...

In this paper we study the application of bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of bayesian networks as: Naive Bayes, Tree Augmented Naive Bayes, Forest Augmented Naive Bayes and General Bayesian Networks, are applied in the classification of hyperspectral data. In addition, several bayesian multi-net models are applied in th...

So causal models need to be able to treat causal relationships as causes and effects. This observation motivates an extension the Bayesian network causal calculus (Section 2) to allow nodes that themselves take Bayesian networks as values. Such networks will be called recursive Bayesian networks (Section 3). Because recursive Bayesian networks make causal and probabilistic claims at different l...