Bayesian Networks: An Introduction provides a self-contained
introduction to the theory and applications of Bayesian networks, a
topic of interest and importance for statisticians, computer
scientists and those involved in modelling complex data sets. The
material has been extensively tested in classroom teaching and
assumes a basic knowledge of probability, statistics and
mathematics. All notions are carefully explained and feature
exercises throughout.
Features include:
* An introduction to Dirichlet Distribution, Exponential Families
and their applications.
* A detailed description of learning algorithms and Conditional
Gaussian Distributions using Junction Tree methods.
* A discussion of Pearl’s intervention calculus, with an
introduction to the notion of see and do conditioning.
* All concepts are clearly defined and illustrated with examples
and exercises. Solutions are provided online.
This book will prove a valuable resource for postgraduate
students of statistics, computer engineering, mathematics, data
mining, artificial intelligence, and biology.
Researchers and users of comparable modelling or statistical
techniques such as neural networks will also find this book of
interest.

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