Augmenting learning function to Bayesian network inferences with maximum likelihood parameters

Authors:
WeiYi Liu;Kun Yue;JiaDong Zhang
Affiliations:
Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, PR China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, PR China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Computing the posterior probability distribution for a set of query variables by search result is an important task of inferences with a Bayesian network. Starting from real applications, it is also necessary to make inferences when the evidence is not contained in training data. In this paper, we are to augment the learning function to Bayesian network inferences, and extend the classical ''search''-based inferences to ''search+learning''-based inferences. Based on the support vector machine, we use a class of hyperplanes to construct the hypothesis space. Then we use the method of solving an optimal hyperplane to find a maximum likelihood hypothesis for the value not contained in training data. Further, we give a convergent Gibbs sampling algorithm for approximate probabilistic inference with the presence of maximum likelihood parameters. Preliminary experiments show the feasibility of our proposed methods.