Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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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Bayesian Decision Problems
Graphical Models 20
Two Equivalent Irrelevance Criteria
Fundamental Rule and Bayes Rule
Reasoning Under Uncertainty
Identifying the Variables of a Model
Model Verification 1 59
Batch Parameter Learning From Data
onflict Analysis 261
Decision Making Under Uncertainty
ObjectOriented Probabilistic Networks
olving Probabilistic Networks
Walue of Information Analysis