Probabilistic Expert Systems SYNOPSIS
Probabilistic Expert Systems emphasizes the basic computational principles that make probabilistic reasoning feasible in expert systems. The key to computation in these systems is the modularity of the probabilistic model. Shafer describes and compares the principal architectures for exploiting this modularity in the computation of prior and posterior probabilities. He also indicates how these similar yet different architectures apply to a wide variety of other problems of recursive computation in applied mathematics and operations research.
The field of probabilistic expert systems has continued to flourish since the author delivered his lectures on the topic in June 1992, but the understanding of join-tree architectures has remained missing from the literature. This monograph fills this void by providing an analysis of join-tree methods for the computation of prior and posterior probabilities in belief nets. These methods, pioneered in the mid to late 1980s, continue to be central to the theory and practice of probabilistic expert systems. In addition to purely probabilistic expert systems, join-tree methods are also used in expert systems based on Dempster-Shafer belief functions or on possibility measures. Variations are also used for computation in relational databases, in linear optimization, and in constraint satisfaction.
This book describes probabilistic expert systems in a more rigorous and focused way than existing literature, and provides an annotated bibliography that includes pointers to conferences and software. Also included are exercises that will help the reader begin to explore the problem of generalizing from probability to broader domains of recursive computation.
This book will be useful to both active researchers and graduate students in computer science, mathematical statistics, operations research, engineering, and various fields of application of probabilistic expert systems. Computer scientists interested in artificial intelligence will be especially interested in the information contained in this monograph. Statisticians working in Markov chains and Monte Carlo approximation also will find the book useful.
About the Author:
Glenn Shafer is a Professor in the Department of Accounting and Information Systems in the Faculty of Management at Rutgers University. His contributions to the foundations of probabilistic and causal reasoning include his work on Dempster-Shafer theory and more recent work on causal conjecture.
FROM THE CRITICS
D. J. Hand
The monograph describes how modularity in a probabilistic model
enables computation of probabilistic inferences, and outlines the main
architectures for capitalizing on this modularity. It provides a good,
brief, but rigorous introduction to the ideas.
&151; Short Book Reviews
Slawomir T. Wierzchon
This is a clever and concise book guaranteeing a quick and detailed introduction to the domain of probability propagation. Exercises added at the end of each chapter extend its content and allow a reader a deeper understanding of the core ideas. The short comments added to the biographical material included at the end of the book open a perspective to other and new approaches to this problem.
&151; Control Engineering Practice
S. G. Valeev
The reviewing monograph should be useful to scholars and students in
artificial intelligence, operations research and the various branches
of applied statistics that use probabilistic methods.
&151; Zentralblatt fur Mathematik
Booknews
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