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About this product
- DescriptionBayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is w employed across a variety of fields for the purposes of analysis, simulation, prediction and diagsis. 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.
- Author BiographyOlivier Pourret is a research engineer at Electricite de France (EDF) and an analyst at EDF Trading. He has published a number of papers describing his use of Bayesian Belief Networks (BBNs), and co-authors a book on the subject. He also taught reliability modeling at the University of Marne-la-Vallee from 1998 to 2002, and initiated the BBN course at EDF R&D Training Institute in 1999. Patrick Naim is the founder and CEO of Elsewhere, an engineering company specialized in knowledge technologies and quantitative modeling. He also works as a consultant in operational risk modeling for a major French bank, and in design risk modeling for a major US oil company. He is the author or co-author of four books (2 Wiley titles) in data mining, data modeling and BBNs, and he teaches data modeling and Bayesian networks at three Parisian schools. Bruce Marcot is a research wildlife ecologist with the Ecosystems Processes Research Program in the US. He conducts applied scientific research and technology application projects for risk assessment and decision modeling in forest resource and wildlife planning. Author of several papers on the use of BBNs, he is sought for lecturing and teaching short courses on BBN and decision modeling methods.
- PublisherJohn Wiley and Sons Ltd
- Date of Publication20/03/2008
- Series TitleStatistics in Practice
- Place of PublicationHoboken
- Country of PublicationUnited States
- Content NoteIllustrations
- Weight768 g
- Width162 mm
- Height237 mm
- Spine28 mm
- Edited byBruce Marcot,Olivier Pourret,Patrick Naim
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