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

Uffe B. Kjaerulff

ISBN 10: 1441925465 / ISBN 13: 9781441925466

Published by Springer New York, 2007

About this Item

318 pages. 9.00x6.00x0.77 inches. In Stock.

Seller Inventory # x-1441925465

Bibliographic Details

• Title: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
• Publisher: Springer New York
• Publication Date: 2007
• Language: English
• ISBN 10: 1441925465
• ISBN 13: 9781441925466
• Binding: Paperback
• Condition: Brand New
• Weight: 0.58 kilograms

About this title

Synopsis

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. This book 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. 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. Many of the techniques and methods presented have been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.

From the Back Cover

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.