Design of sophisticated Bayesian network systems based on large-scale random fields

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In the research project, we have achieved our goal that we construct the sophisticated methodologies for the design of statistical inference systems based on large scale random fields. The key points of our achievements are to employ some technologies in the cluster variation method in the statistical mechanics. They were not so major techniques of belief propagations in the research felids of probabilistic information processing and statistical learning theory when the present project started in 2006. Our outstanding result in our research project is to publish the book that the general methodology for modeling and designing the Bayesian networks with large scale random fields is systematized.