Bayesian Networks
A Practical Guide to Applications
Statistics in Practice
1. Auflage März 2008
446 Seiten, Hardcover
Wiley & Sons Ltd
Kurzbeschreibung
The first book to focus on BBNs practical applications, Bayesian Belief Networks: A Practical Guide to Applications defines and introduces BBNs, demonstrating how they can be used to forecast, explain, make decisions, and represent knowledge in the context of complex and uncertain systems. The book then provides step-by-step guidelines, specifying model structure and probabilities, tips, cautions and caveats, and model validation. The largest part of the text consists of 20 application chapters demonstrating each type of application in each field in which BBNs are used. The book concludes with a review of the applications, a summary of the lessons learned, and a discussion of the future of the area.
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.
Preface.
1 Introduction to Bayesian networks.
2 Medical diagnosis.
3 Clinical decision support.
4 Complex genetic models.
5 Crime risk factors analysis.
6 Spatial dynamics in the coastal region.
7 Inference problems in forensic science.
8 Conservation of marbled murrelets in British Columbia.
9 Classifiers for modeling of mineral potential.
10 Student modeling.
11 Sensor validation.
12 An information retrieval system.
13 Reliability analysis of systems.
14 Terrorism risk management.
15 Credit-rating of companies..
16 Classification of Chilean wines.
17 Pavement and bridge management.
18 Complex industrial process operation.
19 Probability of default for large corporates.
20 Risk management in robotics.
21 Enhancing Human Cognition.
22 Conclusion.
Bibliography.
Index.
Patrick Naïm 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.
