Bayesian Networks: An Introduction

Timo Koski

ISBN 10: 0470743042 / ISBN 13: 9780470743041

Published by John Wiley & Sons Inc, 2009

About this Item

Bayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks is a self-contained introduction to the theory and applications of Bayesian networks. Series: Wiley Series in Probability and Statistics. Num Pages: 366 pages, Illustrations. BIC Classification: PBT. Category: (U) Tertiary Education (US: College); (UU) Undergraduate. Dimension: 253 x 176 x 26. Weight in Grams: 788. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. Seller Inventory # V9780470743041

Bibliographic Details

Title: Bayesian Networks: An Introduction

Publisher: John Wiley & Sons Inc

Publication Date: 2009

Binding: Hardcover

Condition: New

About this title

Synopsis:

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.

Review:

"It assumes only a basic knowledge of probability, statistics and mathematics and is well suited for classroom teaching ... Each chapter of the book is concluded with short notes on the literature and a set of helpful exercises." (Mathematical Reviews, 2011) "Extensively tested in classroom teaching ... .The authors clearly define all concepts and provide numerous examples and exercises." ( Book News , December 2009)