Pourret / Naïm / Marcot Bayesian Networks

A Practical Guide to Applications
1. Auflage 2008
ISBN: 978-0-470-99454-2
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 446 Seiten, E-Book

Reihe: Statistics in Practice

Bayesian Networks, the result of the convergence of artificialintelligence with statistics, are growing in popularity. Theirversatility and modelling power is now employed across a variety offields for the purposes of analysis, simulation, prediction anddiagnosis.
This book provides a general introduction to Bayesian networks,defining and illustrating the basic concepts with pedagogicalexamples and twenty real-life case studies drawn from a range offields including medicine, computing, natural sciences andengineering.
Designed to help analysts, engineers, scientists andprofessionals taking part in complex decision processes tosuccessfully implement Bayesian networks, this book equips readerswith proven methods to generate, calibrate, evaluate and validateBayesian networks.
The book:
* Provides the tools to overcome common practical challenges suchas the treatment of missing input data, interaction with expertsand decision makers, determination of the optimal granularity andsize of the model.
* Highlights the strengths of Bayesian networks whilst alsopresenting a discussion of their limitations.
* Compares Bayesian networks with other modelling techniques suchas neural networks, fuzzy logic and fault trees.
* Describes, for ease of comparison, the main features of themajor Bayesian network software packages: Netica, Hugin, Elvira andDiscoverer, from the point of view of the user.
* Offers a historical perspective on the subject and analysesfuture directions for research.
Written by leading experts with practical experience of applyingBayesian networks in finance, banking, medicine, robotics, civilengineering, geology, geography, genetics, forensic science,ecology, and industry, the book has much to offer bothpractitioners and researchers involved in statistical analysis ormodelling in any of these fields.

Autoren/Hrsg.

Pourret, Olivier

Naïm, Patrick

Marcot, Bruce

Über Autor(innen)

Olivier Pourret is aresearch engineer at Électricité de France (EDF) and ananalyst at EDF Trading. He has published a number of papersdescribing his use of Bayesian Belief Networks (BBNs), andco-authors a book on the subject. He also taught reliabilitymodeling at the University of Marne-la-Vallée from 1998 to2002, and initiated the BBN course at EDF R&D TrainingInstitute in 1999.
Patrick Naïm is the founder and CEO of Elsewhere, anengineering company specialized in knowledge technologies andquantitative modeling. He also works as a consultant in operationalrisk modeling for a major French bank, and in design risk modelingfor a major US oil company. He is the author or co-author of fourbooks (2 Wiley titles) in data mining, data modeling and BBNs, andhe teaches data modeling and Bayesian networks at three Parisianschools.
Bruce Marcot is a research wildlife ecologist with theEcosystems Processes Research Program in the US. He conductsapplied scientific research and technology application projects forrisk assessment and decision modeling in forest resource andwildlife planning. Author of several papers on the use of BBNs, heis sought for lecturing and teaching short courses on BBN anddecision modeling methods.