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About this product
Product Identifiers
PublisherSpringer New York
ISBN-101461274818
ISBN-139781461274810
eBay Product ID (ePID)143593695
Product Key Features
Number of PagesXiv, 605 Pages
Publication NameExpert Systems and Probabilistic Network Models
LanguageEnglish
Publication Year2011
SubjectExpert Systems, Probability & Statistics / General, Intelligence (Ai) & Semantics
TypeTextbook
AuthorJose M. Gutierrez, Enrique Castillo, Ali S. Hadi
Subject AreaMathematics, Computers
SeriesMonographs in Computer Science Ser.
FormatTrade Paperback
Dimensions
Item Height0.5 in
Item Weight32.8 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
Dewey Edition20
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal006.3/3
Table Of ContentPreface.- 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.
SynopsisArtificial 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.
LC Classification NumberQ334-342
