Probabilistic Networks And Expert Systems

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Probabilistic Networks and Expert Systems

Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Probabilistic Networks and Expert Systems Book PDF

Author : Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 48,5 Mb
Release : 2007-07-16
Category : Computers
ISBN : 0387718230

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Probabilistic Networks and Expert Systems by Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter Pdf

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.

Probabilistic Networks and Expert Systems

Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Probabilistic Networks and Expert Systems Book PDF

Author : Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Publisher : Springer
Page : 324 pages
File Size : 52,7 Mb
Release : 2007-07-25
Category : Mathematics
ISBN : 0387718265

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Probabilistic Networks and Expert Systems by Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter Pdf

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.

Expert Systems and Probabilistic Network Models

Enrique Castillo,Jose M. Gutierrez,Ali S. Hadi
Expert Systems and Probabilistic Network Models Book PDF

Author : Enrique Castillo,Jose M. Gutierrez,Ali S. Hadi
Publisher : Springer Science & Business Media
Page : 612 pages
File Size : 46,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461222705

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Expert Systems and Probabilistic Network Models by Enrique Castillo,Jose M. Gutierrez,Ali S. Hadi Pdf

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.

Probabilistic Reasoning in Expert Systems

Richard E. Neapolitan
Probabilistic Reasoning in Expert Systems Book PDF

Author : Richard E. Neapolitan
Publisher : CreateSpace
Page : 448 pages
File Size : 43,6 Mb
Release : 2012-06-01
Category : Computers
ISBN : 1477452540

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Probabilistic Reasoning in Expert Systems by Richard E. Neapolitan Pdf

This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.

Probabilistic Reasoning in Intelligent Systems

Judea Pearl
Probabilistic Reasoning in Intelligent Systems Book PDF

Author : Judea Pearl
Publisher : Elsevier
Page : 552 pages
File Size : 53,7 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9780080514895

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Probabilistic Reasoning in Intelligent Systems by Judea Pearl Pdf

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Interactive Collaborative Information Systems

Robert Babuška,Frans C.A. Groen
Interactive Collaborative Information Systems Book PDF

Author : Robert Babuška,Frans C.A. Groen
Publisher : Springer
Page : 586 pages
File Size : 49,6 Mb
Release : 2010-03-22
Category : Technology & Engineering
ISBN : 9783642116889

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Interactive Collaborative Information Systems by Robert Babuška,Frans C.A. Groen Pdf

The increasing complexity of our world demands new perspectives on the role of technology in decision making. Human decision making has its li- tations in terms of information-processing capacity. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and tra?c management, where humans need to engage in close collaborations with arti?cial systems to observe and understand the situation and respond in a sensible way. We believe that close collaborations between humans and arti?cial systems will become essential and that the importance of research into Interactive Collaborative Information Systems (ICIS) is self-evident. Developments in information and communication technology have ra- cally changed our working environments. The vast amount of information available nowadays and the wirelessly networked nature of our modern so- ety open up new opportunities to handle di?cult decision-making situations such as computer-supported situation assessment and distributed decision making. To make good use of these new possibilities, we need to update our traditional views on the role and capabilities of information systems. The aim of the Interactive Collaborative Information Systems project is to develop techniques that support humans in complex information en- ronments and that facilitate distributed decision-making capabilities. ICIS emphasizes the importance of building actor-agent communities: close c- laborations between human and arti?cial actors that highlight their comp- mentary capabilities, and in which task distribution is ?exible and adaptive.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Uffe B. Kjærulff,Anders L. Madsen
Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis Book PDF

Author : Uffe B. Kjærulff,Anders L. Madsen
Publisher : Springer Science & Business Media
Page : 388 pages
File Size : 52,8 Mb
Release : 2012-11-30
Category : Computers
ISBN : 9781461451044

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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis by Uffe B. Kjærulff,Anders L. Madsen Pdf

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.

Learning in Graphical Models

M.I. Jordan
Learning in Graphical Models Book PDF

Author : M.I. Jordan
Publisher : Springer Science & Business Media
Page : 630 pages
File Size : 49,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9789401150149

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Learning in Graphical Models by M.I. Jordan Pdf

In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.

Bayesian Artificial Intelligence

Kevin B. Korb,Ann E. Nicholson
Bayesian Artificial Intelligence Book PDF

Author : Kevin B. Korb,Ann E. Nicholson
Publisher : Chapman and Hall/CRC
Page : 392 pages
File Size : 43,8 Mb
Release : 2003-09-25
Category : Computers
ISBN : 1584883871

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Bayesian Artificial Intelligence by Kevin B. Korb,Ann E. Nicholson Pdf

As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors’ website.

Probabilistic Similarity Networks

David E. Heckerman
Probabilistic Similarity Networks Book PDF

Author : David E. Heckerman
Publisher : MIT Press (MA)
Page : 272 pages
File Size : 46,8 Mb
Release : 1991
Category : Computers
ISBN : UOM:39015025008452

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Probabilistic Similarity Networks by David E. Heckerman Pdf

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.

Bayesian Networks

Marco Scutari,Jean-Baptiste Denis
Bayesian Networks Book PDF

Author : Marco Scutari,Jean-Baptiste Denis
Publisher : CRC Press
Page : 275 pages
File Size : 45,5 Mb
Release : 2021-07-28
Category : Computers
ISBN : 9781000410389

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Bayesian Networks by Marco Scutari,Jean-Baptiste Denis Pdf

Explains the material step-by-step starting from meaningful examples Steps detailed with R code in the spirit of reproducible research Real world data analyses from a Science paper reproduced and explained in detail Examples span a variety of fields across social and life sciences Overview of available software in and outside R

Probabilistic Methods for Bioinformatics

Richard E. Neapolitan
Probabilistic Methods for Bioinformatics Book PDF

Author : Richard E. Neapolitan
Publisher : Morgan Kaufmann
Page : 424 pages
File Size : 54,5 Mb
Release : 2009-06-12
Category : Computers
ISBN : 0080919367

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Probabilistic Methods for Bioinformatics by Richard E. Neapolitan Pdf

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis. Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics. Shares insights about when and why probabilistic methods can and cannot be used effectively; Complete review of Bayesian networks and probabilistic methods with a practical approach.

Modeling and Reasoning with Bayesian Networks

Adnan Darwiche
Modeling and Reasoning with Bayesian Networks Book PDF

Author : Adnan Darwiche
Publisher : Cambridge University Press
Page : 561 pages
File Size : 50,9 Mb
Release : 2009-04-06
Category : Computers
ISBN : 9780521884389

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Modeling and Reasoning with Bayesian Networks by Adnan Darwiche Pdf

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Uffe B. Kjærulff,Anders L. Madsen
Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis Book PDF

Author : Uffe B. Kjærulff,Anders L. Madsen
Publisher : Springer Science & Business Media
Page : 318 pages
File Size : 42,6 Mb
Release : 2007-12-20
Category : Computers
ISBN : 9780387741017

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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis by Uffe B. Kjærulff,Anders L. Madsen Pdf

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.

Probabilistic Reasoning in Expert Systems

Richard E. Neapolitan
Probabilistic Reasoning in Expert Systems Book PDF

Author : Richard E. Neapolitan
Publisher : Wiley-Interscience
Page : 492 pages
File Size : 50,7 Mb
Release : 1990-03-16
Category : Computers
ISBN : UOM:39015017023329

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Probabilistic Reasoning in Expert Systems by Richard E. Neapolitan Pdf

Addresses the use probability theory as a tool for designing with and implementing uncertainity reasoning. Provides many concrete algorithms, explores techniques for solving multimembership classification problems not based directly on causal networks, and offers practical recommendations, matching specific methods with sample expert systems.