Bayesian Networks: A Practical Guide to Applications

Bayesian Networks: A Practical Guide to Applications

Editor(s): Olivier Pourret, Patrick Naim, Bruce Marcot

Published Online: 19 MAR 2008

Print ISBN: 9780470060308

Online ISBN: 9780470994559

DOI: 10.1002/9780470994559

About this Book

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.

Table of contents

    1. You have free access to this content
    2. Chapter 3

      Decision Support for Clinical Cardiovascular Risk Assessment (pages 33–52)

      Ann E. Nicholson, Charles R. Twardy, Kevin B. Korb and Lucas R. Hope

    3. Chapter 11

      Sensor Validation (pages 187–202)

      Pablo H. Ibargüengoytia, L. Enrique Sucar and Sunil Vadera

    4. Chapter 12

      An Information Retrieval System for Parliamentary Documents (pages 203–223)

      Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Carlos Martín and Alfonso E. Romero

    5. Chapter 13

      Reliability Analysis of Systems with Dynamic Dependencies (pages 225–238)

      Andrea Bobbio, Daniele Codetta-Raiteri, Stefania Montani and Luigi Portinale

    6. Chapter 14

      Terrorism Risk Management (pages 239–262)

      David C. Daniels, Linwood D. Hudson, Kathryn B. Laskey, Suzanne M. Mahoney, Bryan S. Ware and Edward J. Wright

    7. Chapter 16

      Classification of Chilean Wines (pages 279–299)

      Manuel A. Duarte-Mermoud, Nicolás H. Beltrán and Sergio H. Vergara

    8. Chapter 20

      Risk Management in Robotics (pages 345–363)

      Anders L. Madsen, Anders L. Kalwa and Uffe B. Kærulff

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