The Resource Expert Systems and Probabilistic Network Models, by Enrique Castillo, Jose M. Gutierrez, Ali S. Hadi, (electronic resource)

Expert Systems and Probabilistic Network Models, by Enrique Castillo, Jose M. Gutierrez, Ali S. Hadi, (electronic resource)

Label
Expert Systems and Probabilistic Network Models
Title
Expert Systems and Probabilistic Network Models
Statement of responsibility
by Enrique Castillo, Jose M. Gutierrez, Ali S. Hadi
Creator
Contributor
Author
Author
Subject
Language
  • eng
  • eng
Summary
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
Member of
http://library.link/vocab/creatorName
Castillo, Enrique
Dewey number
006.3/3
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
  • fPVJzJK_X2k
  • -GvtDofxSxY
  • CnJANJs4QlY
Image bit depth
0
Language note
English
LC call number
Q334-342
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Gutierrez, Jose M.
  • Hadi, Ali S.
Series statement
Monographs in Computer Science,
http://library.link/vocab/subjectName
  • Artificial intelligence
  • Artificial Intelligence
Label
Expert Systems and Probabilistic Network Models, by Enrique Castillo, Jose M. Gutierrez, Ali S. Hadi, (electronic resource)
Instantiates
Publication
Note
"With 250 Illustrations."
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Color
not applicable
Content category
text
Content type code
  • txt
Contents
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
Dimensions
unknown
Edition
1st ed. 1997.
Extent
1 online resource (XIV, 605p. 265 illus.)
File format
multiple file formats
Form of item
online
Isbn
9781461222705
Level of compression
uncompressed
Media category
computer
Media type code
  • c
Other control number
10.1007/978-1-4612-2270-5
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3400000000089889
  • (SSID)ssj0000806203
  • (PQKBManifestationID)11465170
  • (PQKBTitleCode)TC0000806203
  • (PQKBWorkID)10747990
  • (PQKB)11078849
  • (DE-He213)978-1-4612-2270-5
  • (MiAaPQ)EBC3076326
  • (EXLCZ)993400000000089889
Label
Expert Systems and Probabilistic Network Models, by Enrique Castillo, Jose M. Gutierrez, Ali S. Hadi, (electronic resource)
Publication
Note
"With 250 Illustrations."
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Color
not applicable
Content category
text
Content type code
  • txt
Contents
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
Dimensions
unknown
Edition
1st ed. 1997.
Extent
1 online resource (XIV, 605p. 265 illus.)
File format
multiple file formats
Form of item
online
Isbn
9781461222705
Level of compression
uncompressed
Media category
computer
Media type code
  • c
Other control number
10.1007/978-1-4612-2270-5
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3400000000089889
  • (SSID)ssj0000806203
  • (PQKBManifestationID)11465170
  • (PQKBTitleCode)TC0000806203
  • (PQKBWorkID)10747990
  • (PQKB)11078849
  • (DE-He213)978-1-4612-2270-5
  • (MiAaPQ)EBC3076326
  • (EXLCZ)993400000000089889

Library Locations

  • Architecture LibraryBorrow it
    Gould Hall 830 Van Vleet Oval Rm. 105, Norman, OK, 73019, US
    35.205706 -97.445050
  • Bizzell Memorial LibraryBorrow it
    401 W. Brooks St., Norman, OK, 73019, US
    35.207487 -97.447906
  • Boorstin CollectionBorrow it
    401 W. Brooks St., Norman, OK, 73019, US
    35.207487 -97.447906
  • Chinese Literature Translation ArchiveBorrow it
    401 W. Brooks St., RM 414, Norman, OK, 73019, US
    35.207487 -97.447906
  • Engineering LibraryBorrow it
    Felgar Hall 865 Asp Avenue, Rm. 222, Norman, OK, 73019, US
    35.205706 -97.445050
  • Fine Arts LibraryBorrow it
    Catlett Music Center 500 West Boyd Street, Rm. 20, Norman, OK, 73019, US
    35.210371 -97.448244
  • Harry W. Bass Business History CollectionBorrow it
    401 W. Brooks St., Rm. 521NW, Norman, OK, 73019, US
    35.207487 -97.447906
  • History of Science CollectionsBorrow it
    401 W. Brooks St., Rm. 521NW, Norman, OK, 73019, US
    35.207487 -97.447906
  • John and Mary Nichols Rare Books and Special CollectionsBorrow it
    401 W. Brooks St., Rm. 509NW, Norman, OK, 73019, US
    35.207487 -97.447906
  • Library Service CenterBorrow it
    2601 Technology Place, Norman, OK, 73019, US
    35.185561 -97.398361
  • Price College Digital LibraryBorrow it
    Adams Hall 102 307 West Brooks St., Norman, OK, 73019, US
    35.210371 -97.448244
  • Western History CollectionsBorrow it
    Monnet Hall 630 Parrington Oval, Rm. 300, Norman, OK, 73019, US
    35.209584 -97.445414
Processing Feedback ...