Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. Probabilistic network models (in particular Bayesian networks) have emerged in the last two decades as a sound and efficient methodology to deal with this problem combining graphs (to intuitively represent dependencies) and probabilities (to quantify these dependencies). The success of this metodology hinges in the use of a single probabilistic joint model build from a simplified structure given by the graph, but considering all the relevant information of a given problem.
This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students including both a rigurous theoretical treatment and many illustrative examples and applications.
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