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140122 ||| eng |
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|a 9781461222705
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| 100 |
1 |
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|a Castillo, Enrique
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| 245 |
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|a Expert Systems and Probabilistic Network Models
|h Elektronische Ressource
|c by Enrique Castillo, Jose M. Gutierrez, Ali S. Hadi
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| 250 |
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|a 1st ed. 1997
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| 260 |
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|a New York, NY
|b Springer New York
|c 1997, 1997
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| 300 |
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|a XIV, 605 p
|b online resource
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| 505 |
0 |
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|a 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 --
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| 505 |
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|a 12.5 Damage of Concrete Structures: The Gaussian Model -- Exercises -- List of Notation -- References
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| 505 |
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|a 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 --
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| 505 |
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|a 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 --
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| 505 |
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|a 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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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Artificial intelligence
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| 700 |
1 |
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|a Gutierrez, Jose M.
|e [author]
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| 700 |
1 |
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|a Hadi, Ali S.
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
0 |
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|a Monographs in Computer Science
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| 028 |
5 |
0 |
|a 10.1007/978-1-4612-2270-5
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4612-2270-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a 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
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