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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)
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Overview Of Bayesian Network (1st ed.). (R. Rauda, Ed.) Lambert Academic Publishing
Bayesian network is a combination of probabilistic model and graph model. It is applied widely in machine learning, data mining, diagnosis, etc. because it has a solid evidence-based inference which is familiar to human intuition. However, Bayesian network may cause confusions because there are many complicated concepts, formulas and diagrams relating to it. Such concepts should be organized and presented in such a clear manner that understanding it is easy. This is the goal of this report. The report includes 5 main sections that cover principles of Bayesian network. The section 1 is an introduction to Bayesian network giving some basic concepts. Advanced concepts are mentioned in section 2. Inference mechanism of Bayesian network is described in section 3. Parameter learning which tells us how to update parameters of Bayesian network is described in section 4. Section 5 focuses on structure learning which mentions how to build up Bayesian network. In general, three main subjects of Bayesian network are inference, parameter learning, and structure learning which are mentioned in successive sections 3, 4, and 5. Section 6 is the conclusion.
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Uncertainty Proceedings 1994
