Expert Systems and Probabilistic Network Models

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Springer Science & Business Media, Dec 13, 1996 - Computers - 605 pages
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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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Contents

Introduction
1
12 What Is an Expert System?
2
13 Motivating Examples
3
14 Why Expert Systems?
7
15 Types of Expert System
8
16 Components of an Expert System
10
17 Developing an Expert System
14
18 Other Areas of AI
16
77 Conditionally Specified Probabilistic Models
298
Exercises
311
Exact Propagation in Probabilistic Network Models
317
82 Propagation of Evidence
318
83 Propagation in Polytrees
321
84 Propagation in MultiplyConnected Networks
342
86 Clustering Methods
351
87 Propagation Using Join Trees
366

19 Concluding Remarks
20
RuleBased Expert Systems
21
22 The Knowledge Base
22
23 The Inference Engine
28
24 Coherence Control
48
25 Explaining Conclusions
52
26 Some Applications
53
27 Introducing Uncertainty
65
Probabilistic Expert Systems
69
32 Some Concepts in Probability Theory
71
33 Generalized Rules
85
34 Introducing Probabilistic Expert Systems
86
35 The Knowledge Base
91
36 The Inference Engine
102
37 Coherence Control
104
38 Comparing RuleBased and Probabilistic Expert Systems
106
Exercises
108
Some Concepts of Graphs
113
42 Basic Concepts and Definitions
114
43 Characteristics of Undirected Graphs
118
44 Characteristics of Directed Graphs
122
45 Triangulated Graphs
129
46 Cluster Graphs
139
47 Representation of Graphs
144
48 Some Useful Graph Algorithms
158
Exercises
172
Building Probabilistic Models
175
52 Graph Separation
177
53 Some Properties of Conditional Independence
184
54 Special Types of Input Lists
192
55 Factorizations of the JPD
195
56 Constructing the JPD
200
Appendix to Chapter 5
204
Exercises
206
Graphically Specified Models
211
62 Some Definitions and Questions
213
63 Undirected Graph Dependency Models
218
64 Directed Graph Dependency Models
237
65 Independence Equivalent Graphical Models
252
66 Expressiveness of Graphical Models
259
Exercises
262
Extending Graphically Specified Models
267
72 Models Specified by Multiple Graphs
269
73 Models Specified by Input Lists
275
74 Multifactorized Probabilistic Models
279
76 Multifactorized Normal Models
292
88 GoalOriented Propagation
377
89 Exact Propagation in Gaussian Networks
382
Exercises
387
Approximate Propagation Methods
393
92 Intuitive Basis of Simulation Methods
394
93 General Frame for Simulation Methods
400
94 AcceptanceRejection Sampling Method
406
95 Uniform Sampling Method
409
96 The Likelihood Weighing Sampling Method
411
97 BackwardForward Sampling Method
413
98 Markov Sampling Method
415
99 Systematic Sampling Method
419
910 Maximum Probability Search Method
429
911 Complexity Analysis
439
Exercises
440
Symbolic Propagation of Evidence
443
102 Notation and Basic Framework
445
103 Automatic Generation of Symbolic Code
447
104 Algebraic Structure of Probabilities
454
105 Symbolic Propagation Through Numeric Computations
455
106 GoalOriented Symbolic Propagation
464
107 Symbolic Treatment of Random Evidence
470
108 Sensitivity Analysis
472
109 Symbolic Propagation in Gaussian Bayesian Networks
474
Exercises
478
Learning Bayesian Networks
481
112 Measuring the Quality of a Bayesian Network Model
484
113 Bayesian Quality Measures
486
114 Bayesian Measures for Multinomial Networks
490
115 Bayesian Measures for Multinomial Networks
499
116 Minimum Description Length Measures
506
117 Information Measures
509
119 Bayesian Network Search Algorithms
511
1110 The Case of Incomplete Data
513
Bayesian Statistics
515
Exercises
525
Case Studies
529
122 Pressure Tank System
530
123 Power Distribution System
542
124 Damage of Concrete Structures
550
The Gaussian Model
562
Exercises
567
List of Notation
573
References
581
Index
597
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