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Expert Systems and Probabilistic Network Models

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

Bibliographic Details
Main Authors: Castillo, Enrique, Gutierrez, Jose M. (Author), Hadi, Ali S. (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 1997, 1997
Edition:1st ed. 1997
Series:Monographs in Computer Science
Subjects:
Artificial Intelligence
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 7.7 Conditionally Specified Probabilistic Models
  • Exercises
  • 8 Exact Propagation in Probabilistic Network Models
  • 8.1 Introduction
  • 8.2 Propagation of Evidence
  • 8.3 Propagation in Polytrees
  • 8.4 Propagation in Multiply-Connected Networks
  • 8.5 Conditioning Method
  • 8.6 Clustering Methods
  • 8.7 Propagation Using Join Trees
  • 8.8 Goal-Oriented Propagation
  • 8.9 Exact Propagation in Gaussian Networks
  • Exercises
  • 9 Approximate Propagation Methods
  • 9.1 Introduction
  • 9.2 Intuitive Basis of Simulation Methods
  • 9.3 General Frame for Simulation Methods
  • 9.4 Acceptance-Reject ion Sampling Method
  • 9.5 Uniform Sampling Method
  • 9.6 The Likelihood Weighing Sampling Method
  • 9.7 Backward-Forward Sampling Method
  • 9.8 Markov Sampling Method
  • 9.9 Systematic Sampling Method
  • 9.10 Maximum Probability Search Method
  • 9.11 Complexity Analysis
  • Exercises
  • 10 Symbolic Propagation of Evidence
  • 10.1 Introduction
  • 10.2 Notation and Basic Framework
  • 12.5 Damage of Concrete Structures: The Gaussian Model
  • Exercises
  • List of Notation
  • References
  • 10.3 Automatic Generation of Symbolic Code
  • 10.4 Algebraic Structure of Probabilities
  • 10.5 Symbolic Propagation Through Numeric Computations
  • 10.6 Goal-Oriented Symbolic Propagation
  • 10.7 Symbolic Treatment of Random Evidence
  • 10.8 Sensitivity Analysis
  • 10.9 Symbolic Propagation in Gaussian Bayesian Networks
  • Exercises
  • 11 Learning Bayesian Networks
  • 11.1 Introduction
  • 11.2 Measuring the Quality of a Bayesian Network Model
  • 11.3 Bayesian Quality Measures
  • 11.4 Bayesian Measures for Multinomial Networks
  • 11.5 Bayesian Measures for Multinormal Networks
  • 11.6 Minimum Description Length Measures
  • 11.7 Information Measures
  • 11.8 Further Analyses of Quality Measures
  • 11.9 Bayesian Network Search Algorithms
  • 11.10 The Case of Incomplete Data
  • Appendix to Chapter 11: Bayesian Statistics
  • Exercises
  • 12 Case Studies
  • 12.1 Introduction
  • 12.2 Pressure Tank System
  • 12.3 Power Distribution System
  • 12.4 Damage of Concrete Structures
  • 4.4 Characteristics of Directed Graphs
  • 4.5 Triangulated Graphs
  • 4.6 Cluster Graphs
  • 4.7 Representation of Graphs
  • 4.8 Some Useful Graph Algorithms
  • Exercises
  • 5 Building Probabilistic Models
  • 5.1 Introduction
  • 5.2 Graph Separation
  • 5.3 Some Properties of Conditional Independence
  • 5.4Special Types of Input Lists
  • 5.5 Factorizations of the JPD
  • 5.6 Constructing the JPD
  • Appendix to Chapter 5
  • Exercises
  • 6 Graphically Specified Models
  • 6.1 Introduction
  • 6.2 Some Definitions and Questions
  • 6.3 Undirected Graph Dependency Models
  • 6.4 Directed Graph Dependency Models
  • 6.5 Independence Equivalent Graphical Models
  • 6.6 Expressiveness of Graphical Models
  • Exercises
  • 7 Extending Graphically Specified Models
  • 7.1 Introduction
  • 7.2 Models Specified by Multiple Graphs
  • 7.3 Models Specified by Input Lists
  • 7.4 Multifactorized Probabilistic Models
  • 7.5 Multifactorized Multinomial Models
  • 7.6 Multifactorized Normal Models
  • Preface
  • 1 Introduction
  • 1.1 Introduction
  • 1.2 What Is an Expert System?
  • 1.3 Motivating Examples
  • 1.4 Why Expert Systems?
  • 1.5 Types of Expert System
  • 1.6 Components of an Expert System
  • 1.7 Developing an Expert System
  • 1.8 Other Areas of AI
  • 1.9 Concluding Remarks
  • 2 Rule-Based Expert Systems
  • 2.1 Introduction
  • 2.2 The Knowledge Base
  • 2.3 The Inference Engine
  • 2.4 Coherence Control
  • 2.5 Explaining Conclusions
  • 2.6 Some Applications
  • 2.7 Introducing Uncertainty
  • Exercises
  • 3 Probabilistic Expert Systems
  • 3.1 Introduction
  • 3.2 Some Concepts in Probability Theory
  • 3.3 Generalized Rules
  • 3.4 Introducing Probabilistic Expert Systems
  • 3.5 The Knowledge Base
  • 3.6 The Inference Engine
  • 3.7 Coherence Control
  • 3.8 Comparing Rule-Based and Probabilistic Expert Systems
  • Exercises
  • 4 Some Concepts of Graphs
  • 4.1 Introduction
  • 4.2 Basic Concepts and Definitions
  • 4.3 Characteristics of Undirected Graphs

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