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Expert Systems and Probabilistic Network Models

Expert Systems and Probabilistic Network Models Author Enrique Castillo
ISBN-10 9781461222705
Release 2012-12-06
Pages 605
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Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.



Principles of Knowledge Representation and Reasoning

Principles of Knowledge Representation and Reasoning Author A. G. Cohn
ISBN-10 UOM:39015053130467
Release 2000
Pages 736
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Principles of Knowledge Representation and Reasoning has been writing in one form or another for most of life. You can find so many inspiration from Principles of Knowledge Representation and Reasoning also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Principles of Knowledge Representation and Reasoning book for free.



Advances in Bayesian Networks

Advances in Bayesian Networks Author José A. Gámez
ISBN-10 9783540398790
Release 2013-06-29
Pages 328
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In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.



Resilience Engineering

Resilience Engineering Author Nii Attoh-Okine
ISBN-10 9780521193498
Release 2016-04-11
Pages 202
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The book is intended for readers who have backgrounds in probability. It is suitable for practicing engineers, analysts, and researchers.



Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems Author Robert G. Cowell
ISBN-10 0387718230
Release 2007-07-16
Pages 324
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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.



Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis

Bayesian Networks and Influence Diagrams  A Guide to Construction and Analysis Author Uffe B. Kjærulff
ISBN-10 9781461451044
Release 2012-11-30
Pages 382
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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.



Journal of Engineering for Gas Turbines and Power

Journal of Engineering for Gas Turbines and Power Author
ISBN-10 UCSD:31822032911349
Release 2006
Pages
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Journal of Engineering for Gas Turbines and Power has been writing in one form or another for most of life. You can find so many inspiration from Journal of Engineering for Gas Turbines and Power also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Journal of Engineering for Gas Turbines and Power book for free.



Probabilistic reasoning parameter estimation and issues in turbo decoding

Probabilistic reasoning  parameter estimation and issues in turbo decoding Author Saejoon Kim
ISBN-10 CORNELL:31924083813927
Release 1998
Pages 246
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Probabilistic reasoning parameter estimation and issues in turbo decoding has been writing in one form or another for most of life. You can find so many inspiration from Probabilistic reasoning parameter estimation and issues in turbo decoding also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Probabilistic reasoning parameter estimation and issues in turbo decoding book for free.



Probabilistic Expert Systems

Probabilistic Expert Systems Author Glenn Shafer
ISBN-10 1611970040
Release 1996
Pages 80
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Probabilistic Expert Systems emphasizes the basic computational principles that make probabilistic reasoning feasible in expert systems. The key to computation in these systems is the modularity of the probabilistic model. Shafer describes and compares the principal architectures for exploiting this modularity in the computation of prior and posterior probabilities. He also indicates how these similar yet different architectures apply to a wide variety of other problems of recursive computation in applied mathematics and operations research. The field of probabilistic expert systems has continued to flourish since the author delivered his lectures on the topic in June 1992, but the understanding of join-tree architectures has remained missing from the literature. This monograph fills this void by providing an analysis of join-tree methods for the computation of prior and posterior probabilities in belief nets. These methods, pioneered in the mid to late 1980s, continue to be central to the theory and practice of probabilistic expert systems. In addition to purely probabilistic expert systems, join-tree methods are also used in expert systems based on Dempster-Shafer belief functions or on possibility measures. Variations are also used for computation in relational databases, in linear optimization, and in constraint satisfaction. This book describes probabilistic expert systems in a more rigorous and focused way than existing literature, and provides an annotated bibliography that includes pointers to conferences and software. Also included are exercises that will help the reader begin to explore the problem of generalizing from probability to broader domains of recursive computation.



Advances in Case Based Reasoning

Advances in Case Based Reasoning Author Enrico Blanzieri
ISBN-10 UOM:39015048228681
Release 2000-10-02
Pages 530
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This book constitutes the refereed proceedings of the 5th European Workshop on Case-Based Reasonning, EWCBR 2000, held in Trento, Italy in September 2000. The 40 revised full papers presented together with two invited contributions were carefully reviewed and selected for inclusion in the book. All curves issues in case-based reasoning, ranging from foundational and theoretical aspects to advanced applications in various fields are addressed.



Readings in Fuzzy Sets for Intelligent Systems

Readings in Fuzzy Sets for Intelligent Systems Author Didier J. Dubois
ISBN-10 9781483214504
Release 2014-05-12
Pages 928
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Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.



Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning Author David Barber
ISBN-10 9780521518147
Release 2012-02-02
Pages 697
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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.



Subject Guide to Books in Print

Subject Guide to Books in Print Author
ISBN-10 STANFORD:36105025888517
Release 2003
Pages
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Subject Guide to Books in Print has been writing in one form or another for most of life. You can find so many inspiration from Subject Guide to Books in Print also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Subject Guide to Books in Print book for free.



Data Mining Know It All

Data Mining  Know It All Author Soumen Chakrabarti
ISBN-10 0080877885
Release 2008-10-31
Pages 480
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This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.



Regression Analysis by Example

Regression Analysis by Example Author Samprit Chatterjee
ISBN-10 9780470055458
Release 2006-10-20
Pages 416
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The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.



Graphical Models for Machine Learning and Digital Communication

Graphical Models for Machine Learning and Digital Communication Author Brendan J. Frey
ISBN-10 026206202X
Release 1998
Pages 195
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Content Description. #Includes bibliographical references and index.



Algebraic Geometry and Statistical Learning Theory

Algebraic Geometry and Statistical Learning Theory Author Sumio Watanabe
ISBN-10 9780521864671
Release 2009-08-13
Pages 286
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Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.