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Comprehensive introduction to probabilistic networks
Written specifically for practitioners of applied artificial intelligence
Complete guide to understand, construct, and analyze probabilistic networks
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.
Uffe B. Kjærulff holds a PhD on probabilistic networks and is an Associate Professor of Computer Science at Aalborg University. Anders L. Madsen holds a PhD on probabilistic networks and is the CEO of HUGIN Expert A/S.
Content Level »Professional/practitioner
Keywords »Bayesian network - Information - artificial intelligence - calculus - classification - data mining - influence diagram - intelligence - intelligent systems - learning - modeling - probabilistic graphical model - probabilistic network - uncertainty - verification
Fundamentals.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Model Construction.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Model Analysis.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.