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Bayesian Networks And Influence Diagrams A Guide To Construction And Analysis

Author: Uffe B. Kjærulff
Publisher: Springer Science & Business Media
ISBN: 1461451043
Size: 44.81 MB
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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.

Best Practices For The Knowledge Society Knowledge Learning Development And Technology For All

Author: Miltiadis D. Lytras
Publisher: Springer Science & Business Media
ISBN: 3642047572
Size: 62.59 MB
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It is a great pleasure to share with you the Springer LNCS proceedings of the Second World Summit on the Knowledge Society, WSKS 2009, organized by the Open - search Society, Ngo, http://www.open-knowledge-society.org, and held in Samaria Hotel, in the beautiful city of Chania in Crete, Greece, September 16–18, 2009. The 2nd World Summit on the Knowledge Society (WSKS 2009) was an inter- tional scientific event devoted to promoting dialogue on the main aspects of the knowledge society towards a better world for all. The multidimensional economic and social crisis of the last couple of years has brought to the fore the need to discuss in depth new policies and strategies for a human centric developmental processes in the global context. This annual summit brings together key stakeholders involved in the worldwide development of the knowledge society, from academia, industry, and government, including policy makers and active citizens, to look at the impact and prospects of - formation technology, and the knowledge-based era it is creating, on key facets of l- ing, working, learning, innovating, and collaborating in today’s hyper-complex world. The summit provides a distinct, unique forum for cross-disciplinary fertilization of research, favoring the dissemination of research on new scientific ideas relevant to - ternational research agendas such as the EU (FP7), OECD, or UNESCO. We focus on the key aspects of a new sustainable deal for a bold response to the multidimensional crisis of our times.

Statistical Pattern Recognition

Author: Andrew R. Webb
Publisher: John Wiley & Sons
ISBN: 1119961408
Size: 12.49 MB
Format: PDF
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Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: Provides a self-contained introduction to statistical pattern recognition. Includes new material presenting the analysis of complex networks. Introduces readers to methods for Bayesian density estimation. Presents descriptions of new applications in biometrics, security, finance and condition monitoring. Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications Describes mathematically the range of statistical pattern recognition techniques. Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields. www.wiley.com/go/statistical_pattern_recognition

Bayesian Networks

Author: Olivier Pourret
Publisher: John Wiley & Sons
ISBN: 9780470994542
Size: 38.77 MB
Format: PDF, Mobi
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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.

A Mathematical Theory Of Arguments For Statistical Evidence

Author: Paul-André Monney
Publisher: Springer Verlag
ISBN: 9783790815276
Size: 50.40 MB
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The subject of this book is the reasoning under uncertainty based on statistical evidence. The concepts are developed, explained and illustrated in the context of the mathematical theory of hints, which is a variant of the Dempster-Shafer theory of evidence. In the first two chapters, the theory of generalized functional models for a discrete parameter is developed, which leads to a general notion of weight of evidence. The second part of the book is dedicated to the study of special linear functional models called Gaussian linear systems. Finally, it is shown that the celebrated Kalman filter can easily be derived by local propagation of Gaussian hints in a Markov tree.