Expert Systems and Probabilistic Network 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. - 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. Language: English