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Informationen zum Autor Editors OLIVIER POURRET , Electricité de France PATRICK NAÏM , ELSEWARE, France BRUCE MARCOT , USDA Forest Service, Oregon, USA Klappentext 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. Zusammenfassung Split into 4 accessible parts, the book presents: 1. An introduction to and definition of BBNs.2. Step-by-step practical guidelines to applying BBNs.3. A wide variety of applications in industry, natural sciences, services and computing.4. A discussion of the future directions BBN research and applications might take. Inhaltsverzeichnis Foreword ix Preface xi 1 Introduction to Bayesian networks 1 1.1 Models 1 1.2 Probabilistic vs. deterministic models 5 1.3 Unconditional and conditional independence 9 1.4 Bayesian networks 11 2 Medical diagnosis 15 2.1 Bayesian networks in medicine 15 2.2 Context and history 17 2.3 Model construction 19 2.4 Inference 26 2.5 Model validation 28 2.6 Model use 30 2.7 Comparison to other approaches 31 2.8 Conclusions and perspectives 32 3 Clinical decision support 33 3.1 Introduction 33 3.2 Models and methodology 34 3.3 The Busselton network 35 3.4 The PROCAM network 40 3.5 The PROCAM Busselton network 44 3.6 Evaluation 46 3.7 The clinical support tool: TakeHeartII 47 3.8 Conclusion 51 4 Complex genetic models 53 4.1 Introduction 53 4.2 Historical perspectives 54 4.3 Complex traits 56 4.4 Bayesian networks to dissect complex traits 59 4.5 Applications 64 4.6 Future challenges 71 5 Crime risk factors analysis 73 5.1 Introduction 73 5.2 Analysis of the factors affecting crime risk 74 5.3 Expert probabilities elicitation 75 5.4 Data preprocessing 76 5.5 A Bayesian network model 78 5.6 Results 80 5.7 Accuracy assessment 83 5.8 Conclusions 84 6 Spatial dynamics in France...
