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.
