Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis

Author: Uffe B. Kjærulff
Publisher: Springer Science & Business Media
ISBN: 1461451043
Format: PDF, ePub, Docs
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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.

Bayesian Networks and Decision Graphs

Author: Thomas Dyhre Nielsen
Publisher: Springer Science & Business Media
ISBN: 1475735022
Format: PDF, ePub, Mobi
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Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and understand them, and when communicated to a computer, they can easily be compiled. The book emphasizes both the human and the computer side. It gives a thorough introduction to Bayesian networks, decision trees and influence diagrams as well as algorithms and complexity issues.

Probabilistic Graphical Models

Author: Linda C. van der Gaag
Publisher: Springer
ISBN: 3319114336
Format: PDF, Kindle
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This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.

Best Practices for the Knowledge Society Knowledge Learning Development and Technology for All

Author: Miltiadis D. Lytras
Publisher: Springer Science & Business Media
ISBN: 3642047572
Format: PDF, ePub, Docs
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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
Format: PDF, ePub
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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

Applications of Statistics and Probability in Civil Engineering

Author: Michael Faber
Publisher: CRC Press
ISBN: 0203144791
Format: PDF, ePub, Mobi
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Under the pressure of harsh environmental conditions and natural hazards, large parts of the world population are struggling to maintain their livelihoods. Population growth, increasing land utilization and shrinking natural resources have led to an increasing demand of improved efficiency of existing technologies and the development of new ones. Additionally, growing complexities of societal functionalities and interdependencies among infrastructures and urban habitats amplify consequences of malfunctions and failures. Malevolence, sustainable developments and climatic changes have more recently been added to the list of challenges. Over the last decades, substantial progress has been made in assessing and quantifying risks. However, with regard to the broader utilization of risk assessment as a means for societal strategic and operational planning, there is still a great need for further development. Applications of Statistics and Probability in Civil Engineering contains the proceedings of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP11, Zürich, Switzerland, 1-4 August 2011). The book focuses not only on the more traditional technical issues, but also emphasizes the societal context of the decision making problems including the interaction between stakeholders. This holistic perspective supports the enhanced and sustainable allocation of limited resources for the improvement of safety, environment and economy. The book is of interest to researchers and scientists working in the field of risk and reliability in engineering; to professionals and engineers, including insurance and consulting companies working with natural hazards, design, operation and maintenance of civil engineering and industrial facilities; and to decision makers and professionals in the public sector, including nongovernmental organisations responsible for risk management in the public domain, e.g. building authorities, the health and safety executives, the United Nations and the World Economic Forum.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Author: Franco Taroni
Publisher: John Wiley & Sons
ISBN: 1118914740
Format: PDF, Kindle
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"This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation" Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. • Includes self-contained introductions to probability and decision theory. • Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. • Features implementation of the methodology with reference to commercial and academically available software. • Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. • Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. • Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. • Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. • Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Statistical Image Processing and Multidimensional Modeling

Author: Paul Fieguth
Publisher: Springer Science & Business Media
ISBN: 9781441972941
Format: PDF, Kindle
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Images are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a statistical image processing task: the inference of something—an artery, a road, a DNA marker, an oil spill—from imagery, possibly noisy, blurry, or incomplete. A great many textbooks have been written on image processing. However this book does not so much focus on images, per se, but rather on spatial data sets, with one or more measurements taken over a two or higher dimensional space, and to which standard image-processing algorithms may not apply. There are many important data analysis methods developed in this text for such statistical image problems. Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ segmentation, anomaly detection), computer vision (image classification, segmentation), and other 2D/3D problems (biological imaging, porous media). The goal, then, of this text is to address methods for solving multidimensional statistical problems. The text strikes a balance between mathematics and theory on the one hand, versus applications and algorithms on the other, by deliberately developing the basic theory (Part I), the mathematical modeling (Part II), and the algorithmic and numerical methods (Part III) of solving a given problem. The particular emphases of the book include inverse problems, multidimensional modeling, random fields, and hierarchical methods.

Probabilistic Networks and Expert Systems

Author: Robert G. Cowell
Publisher: Springer Science & Business Media
ISBN: 9780387718231
Format: PDF, Kindle
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Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature. Robert G. Cowell is a Lecturer in the Faculty of Actuarial Science and Insurance of the Sir John Cass Business School, City of London. He has been working on probabilistic expert systems since 1989. A. Philip Dawid is Professor of Statistics at Cambridge University. He has served as Editor of the Journal of the Royal Statistical Society (Series B), Biometrika and Bayesian Analysis, and as President of the International Society for Bayesian Analysis. He holds the Royal Statistical Society Guy Medal in Bronze and in Silver, and the Snedecor Award for the Best Publication in Biometry. Steffen L. Lauritzen is Professor of Statistics at the University of Oxford. He has served as Editor of the Scandinavian Journal of Statistics. He holds the Royal Statistical Society Guy Medal in Silver and is an Honorary Fellow of the same society. He has, jointly with David J. Spiegelhalter, received the American Statistical Association’s award for an "Outstanding Statistical Application." David J. Spiegelhalter is Winton Professor of the Public Understanding of Risk at Cambridge University and Senior Scientist in the MRC Biostatistics Unit, Cambridge. He has published extensively on Bayesian methodology and applications, and holds the Royal Statistical Society Guy Medal in Bronze and in Silver.

The Nature of Statistical Learning Theory

Author: Vladimir N. Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475724403
Format: PDF, Docs
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The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.