Name: Expert Systems and Probabilistic Network Models By Enrique Castillo
SKU: 9781461274810
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Expert Systems and Probabilistic Network Models Summary

Expert Systems and Probabilistic Network Models by Enrique Castillo

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.

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.- 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.- 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.- 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.- 12.5 Damage of Concrete Structures: The Gaussian Model.- Exercises.- List of Notation.- References.

Additional information

Sku

NLS9781461274810

ISBN 13

9781461274810

ISBN 10

1461274818

Title

Expert Systems and Probabilistic Network Models by Enrique Castillo

Author

Enrique Castillo

Series

Monographs in Computer Science

Condition

New

Binding type

Paperback

Publisher

Springer-Verlag New York Inc.

Year published

2011-09-15

Number of pages

605

Prizes

N/A

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Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

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This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

Expert Systems and Probabilistic Network Models Enrique Castillo

Expert Systems and Probabilistic Network Models by Enrique Castillo

Summary

Expert Systems and Probabilistic Network Models Summary

Expert Systems and Probabilistic Network Models by Enrique Castillo

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