Full Description
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. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
Contents
Contents: Rule-based expert systems.- Probabilistic expert systems.- Some concepts of graphs.- Building probabalistic models.- Graphically specified models.- Extending graphically specified models.- Exact propagation in probabilistic network models.- Approximate propagation methods.- Symbolic propagation of evidence.- Learning Bayesian models.- Case studies.
