By Uffe B. Kjærulff and Anders L Madsen
ISBN: 978-0-387-74100-0
Series: Information Science and Statistics, Springer
The book is available on Springer’s website.
There is a discussion board at forum.hugin.dk dedicated to this book.
A Guide to Construction and Analysis
About this book
- 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.
Written for: Practitioners
Keywords:
- Bayesian network
- influence diagram
- probabilistic graphical model
Solutions
Solutions to all exercises in the book can be found below.
I Fundamentals
- Introduction
- Networks
Solutions (updated 12 February 2009) - Probabilities
Solutions (updated 12 February 2009) - Probabilistic Networks
Solutions (updated 13 February 2009) - Solving Probabilistic Networks
Solutions (updated 16 February 2009)
II Model Construction
- Eliciting the Model
Solutions (updated 25 July 2009) - Modeling Techniques
Solutions and models (updated 15 February 2009) - Data-Driven Modeling
Solutions and models and data files (updated 4 August 2009)
III Model Analysis