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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. 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.



Table of Contents:
Preface; 1. Multivariate Probability. Probability Distributions; Marginalization; Conditionals; Continuation; Posterior Distributions; Expectation; Classifying Probability Distributions; A Limitation; 2. Construction Sequences. Multiplying Conditionals; DAGs and Belief Nets; Bubble Graphs; Other Graphical Representations; 3. Propagation in Join Trees. Variable-by-Variable Summing Out; The Elementary Architecture; The Shafer-Shenoy Architecture; The Lauritzen-Spiegelhalter Architecture; The Aalborg Architecture; Collect and Distribute; Scope and Alternatives; 4. Resources and References. Meetings; Software; Books; Review Articles; Other Sources; Index.