Bayesian Belief Networks: A Practical Guide to Applications
Pourret, Olivier (Editor)/ Naim, Patrick (Editor)/ Marcot, Bruce (Editor)
ISBN 10: 0470060301 ISBN 13: 9780470060308
Editore: John Wiley & Sons Inc, 2008
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1st edition. 446 pages. 9.00x6.00x1.00 inches. In Stock. This item is printed on demand. Codice articolo __0470060301
Riassunto:
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.
Informazioni sull?autore:
Editors
OLIVIER POURRET, Electricité de France
PATRICK NAÏM, ELSEWARE, France
BRUCE MARCOT, USDA Forest Service, Oregon, USA
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Titolo: Bayesian Belief Networks: A Practical Guide ...
Casa editrice: John Wiley & Sons Inc
Data di pubblicazione: 2008
Legatura: Hardcover
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Bayesian Networks : A Practical Guide to Applications
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ISBN 10: 0470060301
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Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780470060308
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ISBN 10: 0470060301
ISBN 13: 9780470060308
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Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780470060308
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