Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

ISBN-13: 9781441925466 / Angielski / Miękka / 2010 / 318 str.

Uffe B. Kjærulff;Anders L. Madsen

Opis

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.

Recenzje

From the reviews:

"The book under review is by two well known contributors to this general area. ... the book, consisting of five chapters, provides a very clear, insightful introduction to this theory. ... This is a good book for learning or teaching an important, practically useful topic. There are many good examples and example-based discussions." (Jayanta K. Ghosh, International Statistical Review, Vol. 76 (3), 2008)

Kjaerulff, Bayesian networks and influence Diagrams: ...

"This book is written for practitioners wishing to understand, construct, and analyze decision support systems based on Bayesian networks and influence diagrams. ...Simple worked examples are provided throughout....and exercises are included after each chapter. ...this book is a useful addition to the texts on Bayesian networks. ...covers many key aspects in the construction and analysis of decision support systems based on Bayesian networks and influence diagrams, achieving the authors' stated goal; ...I would certainly put this book on my reading list for decision analysis classes and recommend it for research students using these techniques..."(Journal of the American Statistical Association, September 2009, Vol. 104, No. 487)

Spis treści

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.

Blurb

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.