Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis: A Guide to Construction and Analysis (Google eBook)

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Springer Science & Business Media, Dec 20, 2007 - Computers - 336 pages
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Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.
  

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Contents

introduction
3
Bayesian Networks
9
Concluding Remarks
15
Causality
24
Two Equivalent Irrelevance Criteria
31
robabilities
37
Probability Potentials
44
Fundamental Rule and Bayes Rule
50
Model Verification
159
Concluding Remarks
170
Modeling Techniques
177
Probability Distribution Related Techniques
196
Decision Related Techniques
212
Summary
225
Sequential Parameter Learning
252
Conflict Analysis 261
259

Chain Rule
59
Decision Making Under Uncertainty
74
Summary
102
olving Probabilistic Networks
107
Eliciting the Model 143
142
Eliciting the Structure
152
Hypothesis Driven Conflict Analysis
267
ensitivity Analysis 273
274
ralue of Information Analysis
291
ences
305
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