Synopsis
Bayesian networks are now being used in a variety of artificial intelligence applications. These networks are high-level representations of probability distributions over a set of variables that are used for building a model of the problem domain. Bayesian Network Technologies: Applications and Graphical Models provides an excellent and well-balanced collection of areas where Bayesian networks have been successfully applied. This book describes the underlying concepts of Bayesian Networks in an interesting manner with the help of diverse applications, and theories that prove Bayesian networks valid. Bayesian Network Technologies: Applications and Graphical Models provides specific examples of how Bayesian networks are powerful machine learning tools critical in solving real-life problems.
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