Bayesian Networks

<i>Bayesian Networks: An Introduction</i> 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. <p>Features include:</p> <ul type=”disc”> <li>An introduction to Dirichlet Distribution, Exponential Families and their applications.</li> <li>A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.</li> <li>A discussion of Pearl’s intervention calculus, with an introduction to the notion of see and do conditioning.</li> <li>All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.</li> </ul> <p>This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology.</p> <p>Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.</p>