Probabilistic Similarity Networks

Author : David E. Heckerman
Publisher : MIT Press (MA)
ISBN 13 :
Total Pages : 272 pages
Book Rating : 4.52/5 ( download)

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Download or read book Probabilistic Similarity Networks written by David E. Heckerman and published by MIT Press (MA). This book was released on 1991 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this remarkable blend of formal theory and practical application, David Heckerman develops methods for building normative expert systems—expert systems that encode knowledge in a decision-theoretic framework. Heckerman introduces the similarity network and partition, two extensions to the influence diagram representation. He uses the new representations to construct Pathfinder, a large, normative expert system for the diagnosis of lymph-node diseases. Heckerman shows that such expert systems can be built efficiently, and that the use of a normative theory as the framework for representing knowledge can dramatically improve the quality of expertise that is delivered to the user. He concludes with a formal evaluation of the power of his methods for building normative expert systems. David Heckerman is Assistant Professor of Computer Science at the University of Southern California. He received his doctoral degree in Medical Information Sciences from Stanford University. Contents: Introduction. Similarity Networks and Partitions: A Simple Example. Theory of Similarity Networks. Pathfinder: A Case Study. An Evaluation of Pathfinder. Conclusions and Future Work.

Author : Enrique Castillo
Publisher : Springer Science & Business Media
ISBN 13 : 1461222702
Total Pages : 612 pages
Book Rating : 4.05/5 ( download)

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Download or read book Expert Systems and Probabilistic Network Models written by Enrique Castillo and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Robert G. Cowell
Publisher : Springer Science & Business Media
ISBN 13 : 9780387718231
Total Pages : 340 pages
Book Rating : 4.30/5 ( download)

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Download or read book Probabilistic Networks and Expert Systems written by Robert G. Cowell and published by Springer Science & Business Media. This book was released on 2007-07-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.

Author : Daphne Koller
Publisher : MIT Press
ISBN 13 : 0262258358
Total Pages : 1270 pages
Book Rating : 4.57/5 ( download)

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Download or read book Probabilistic Graphical Models written by Daphne Koller and published by MIT Press. This book was released on 2009-07-31 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Author : Thomas Dyhre Nielsen
Publisher : Springer Science & Business Media
ISBN 13 : 0387682821
Total Pages : 457 pages
Book Rating : 4.22/5 ( download)

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Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen and published by Springer Science & Business Media. This book was released on 2009-03-17 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.

Author : Roman Vershynin
Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.94/5 ( download)

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Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Author : Luc De Raedt
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627058427
Total Pages : 191 pages
Book Rating : 4.21/5 ( download)

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Download or read book Statistical Relational Artificial Intelligence written by Luc De Raedt and published by Morgan & Claypool Publishers. This book was released on 2016-03-24 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Author : Salem Benferhat
Publisher : Springer
ISBN 13 : 3540446524
Total Pages : 818 pages
Book Rating : 4.21/5 ( download)

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Download or read book Symbolic and Quantitative Approaches to Reasoning with Uncertainty written by Salem Benferhat and published by Springer. This book was released on 2003-06-30 with total page 818 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2001, held in Toulouse, France in September 2001. The 68 revised full papers presented together with three invited papers were carefully reviewed and selected from over a hundred submissions. The book offers topical sections on decision theory, partially observable Markov decision processes, decision-making, coherent probabilities, Bayesian networks, learning causal networks, graphical representation of uncertainty, imprecise probabilities, belief functions, fuzzy sets and rough sets, possibility theory, merging, belief revision and preferences, inconsistency handling, default logic, logic programming, etc.

Author : Sebastian Thrun
Publisher : MIT Press
ISBN 13 : 0262201623
Total Pages : 668 pages
Book Rating : 4.29/5 ( download)

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Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Author : Otthein Herzog
Publisher : Springer Science & Business Media
ISBN 13 : 9783540650805
Total Pages : 376 pages
Book Rating : 4.06/5 ( download)

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Download or read book KI-98: Advances in Artificial Intelligence written by Otthein Herzog and published by Springer Science & Business Media. This book was released on 1998-09-09 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd Annual German Conference on Artificial Intelligence, KI-98, held in Bremen, Germany, in September 1998. The 16 revised full papers presented were carefully reviewed and selected for inclusion in the proceedings. Also included are three invited papers and abstracts of two invited talks, as well as an appendix containing up-to-date descriptions of German AI projects. Thus the volume gives a unique overview of AI research in Germany.