Bayesian Networks: A Practical Guide to Applications (Statistics in Practice #73)

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis.

This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering.

Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks.

The book:

Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model.

Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations.

Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees.

Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user.

Offers a historical perspective on the subject and analyses future directions for research.

Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

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Title of ebook: Bayesian Networks: A Practical Guide to Applications (Statistics in Practice #73)
ISBN: 9780470994542
parent-ISBN: 9780470060308
Publisher: John Wiley & Sons, Ltd.
Internet download file size: 5453 kb
Pages: 446
Published: 08-2008
Released online for download: 08-04-2008
Editor: Pourret, Olivier
Editor: NaÃ¯m, Patrick
Editor: Marcot, Bruce

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Bayesian Networks

A Practical Guide to Applications

Chapter One

Introduction to Bayesian networks

Olivier Pourret

Electricite de France, 20 Place de la Defense, 92050, Paris la Defense, France

1.1 Models

1.1.1 Definition

The primary objective of this book is to discuss the power and limits of Bayesian networks for the purpose of constructing real-world models. The idea of the authors is not to extensively and formally expound on the formalism of mathematical models, and then explain that these models have been - or may be - applied in various fields; the point of view is, conversely, to explain why and how some recent, real-world problems have been modeled using Bayesian networks, and to analyse what worked and what did not.

Real-world problems - thus the starting point of this chapter - are often described as complex. This term is however seldom defined. It probably makes more sense to say that human cognitive abilities, memory, and reason are limited and that reality is therefore difficu ... read full excerpt from Bayesian Networks: A Practical Guide to Applications (Statistics in Practice #73) ebook

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