Probabilistic Similarity Networks

Download Probabilistic Similarity Networks full books in PDF, epub, and Kindle. Read online Probabilistic Similarity Networks ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Probabilistic Similarity Networks

Author : David E. Heckerman
Publisher : MIT Press (MA)
ISBN 13 :
Total Pages : 272 pages
Book Rating : 4.3/5 (91 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Similarity Networks by : David E. Heckerman

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.

Probabilistic Similarity Networks

Author : David E. Heckerman
Publisher :
ISBN 13 :
Total Pages : 273 pages
Book Rating : 4.:/5 (17 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Similarity Networks by : David E. Heckerman

Download or read book Probabilistic Similarity Networks written by David E. Heckerman and published by . This book was released on 1990 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research suggests strongly that, by identifying specific forms of conditional independence, and by developing representations that exploit these forms of independence for knowledge acquisition, knowledge engineers can construct normative expert systems for domains of larger scope and greater complexity than the domains previously thought to be amendable to the decision-theoretic approach."

Probabilistic Networks and Expert Systems

Author : Robert G. Cowell
Publisher : Springer Science & Business Media
ISBN 13 : 9780387718231
Total Pages : 340 pages
Book Rating : 4.7/5 (182 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Networks and Expert Systems by : Robert G. Cowell

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.

Probabilistic Siamese Networks for Learning Representations

Author : Chen Liu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Siamese Networks for Learning Representations by : Chen Liu

Download or read book Probabilistic Siamese Networks for Learning Representations written by Chen Liu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Networks and Decision Graphs

Author : Thomas Dyhre Nielsen
Publisher : Springer Science & Business Media
ISBN 13 : 0387682821
Total Pages : 457 pages
Book Rating : 4.3/5 (876 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Networks and Decision Graphs by : Thomas Dyhre Nielsen

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.

Probabilistic Graphical Models

Author : Daphne Koller
Publisher : MIT Press
ISBN 13 : 0262258358
Total Pages : 1270 pages
Book Rating : 4.2/5 (622 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Graphical Models by : Daphne Koller

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.

Expert Systems and Probabilistic Network Models

Author : Enrique Castillo
Publisher : Springer Science & Business Media
ISBN 13 : 1461222702
Total Pages : 612 pages
Book Rating : 4.4/5 (612 download)

DOWNLOAD NOW!

Book Synopsis Expert Systems and Probabilistic Network Models by : Enrique Castillo

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.

High-Dimensional Probability

Author : Roman Vershynin
Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.1/5 (84 download)

DOWNLOAD NOW!

Book Synopsis High-Dimensional Probability by : Roman Vershynin

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.

Statistical Relational Artificial Intelligence

Author : Luc De Raedt
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627058427
Total Pages : 191 pages
Book Rating : 4.6/5 (27 download)

DOWNLOAD NOW!

Book Synopsis Statistical Relational Artificial Intelligence by : Luc De Raedt

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.

Probabilistic Logics and Probabilistic Networks

Author : Rolf Haenni
Publisher : Springer Science & Business Media
ISBN 13 : 9400700083
Total Pages : 154 pages
Book Rating : 4.4/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Logics and Probabilistic Networks by : Rolf Haenni

Download or read book Probabilistic Logics and Probabilistic Networks written by Rolf Haenni and published by Springer Science & Business Media. This book was released on 2010-11-19 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.

Probabilistic Conditional Independence

Author : Ramon Sangüesa i Solé
Publisher :
ISBN 13 :
Total Pages : 19 pages
Book Rating : 4.:/5 (85 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Conditional Independence by : Ramon Sangüesa i Solé

Download or read book Probabilistic Conditional Independence written by Ramon Sangüesa i Solé and published by . This book was released on 1996 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Probabilistic Boolean Networks

Author : Ilya Shmulevich
Publisher : SIAM
ISBN 13 : 0898717639
Total Pages : 277 pages
Book Rating : 4.8/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Boolean Networks by : Ilya Shmulevich

Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-01 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science.

Networks and Chaos - Statistical and Probabilistic Aspects

Author : J L Jensen
Publisher : CRC Press
ISBN 13 : 9780412465307
Total Pages : 324 pages
Book Rating : 4.4/5 (653 download)

DOWNLOAD NOW!

Book Synopsis Networks and Chaos - Statistical and Probabilistic Aspects by : J L Jensen

Download or read book Networks and Chaos - Statistical and Probabilistic Aspects written by J L Jensen and published by CRC Press. This book was released on 1993-07-22 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume consists of a collection of tutorial papers by leading experts on statistical and probabilistic aspects of chaos and networks, in particular neural networks. While written for the non-expert, they are intended to bring the reader up to the forefront of knowledge and research in the subject areas concerned. The papers, which contain extensive references to the literature, can separately or in various combinations serve as bases for short- or full-length courses, at graduate or more advanced levels. The papers are directed not only to mathematical statisticians but also to students and researchers in related fields of biology, engineering, geology, physics and probability.

Prediction of Protein Function with a Probabilistic Model for Analysis of Sequence Similarity Networks and Genomic Context

Author : Jeffrey Michael Yunes
Publisher :
ISBN 13 : 9780355857832
Total Pages : 156 pages
Book Rating : 4.8/5 (578 download)

DOWNLOAD NOW!

Book Synopsis Prediction of Protein Function with a Probabilistic Model for Analysis of Sequence Similarity Networks and Genomic Context by : Jeffrey Michael Yunes

Download or read book Prediction of Protein Function with a Probabilistic Model for Analysis of Sequence Similarity Networks and Genomic Context written by Jeffrey Michael Yunes and published by . This book was released on 2018 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effusion GCA extends Effusion by integrating the chief components necessary for automating genomic context analysis. It performs its analysis over a sequence similarity -- functional association network, with a model of protein function that includes a representation of each protein's biological process, performs simultaneous inference on multiple aspects of protein function, and only propagates functional information where it is appropriate.

Networks, Crowds, and Markets

Author : David Easley
Publisher : Cambridge University Press
ISBN 13 : 1139490303
Total Pages : 745 pages
Book Rating : 4.1/5 (394 download)

DOWNLOAD NOW!

Book Synopsis Networks, Crowds, and Markets by : David Easley

Download or read book Networks, Crowds, and Markets written by David Easley and published by Cambridge University Press. This book was released on 2010-07-19 with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are all film stars linked to Kevin Bacon? Why do the stock markets rise and fall sharply on the strength of a vague rumour? How does gossip spread so quickly? Are we all related through six degrees of separation? There is a growing awareness of the complex networks that pervade modern society. We see them in the rapid growth of the internet, the ease of global communication, the swift spread of news and information, and in the way epidemics and financial crises develop with startling speed and intensity. This introductory book on the new science of networks takes an interdisciplinary approach, using economics, sociology, computing, information science and applied mathematics to address fundamental questions about the links that connect us, and the ways that our decisions can have consequences for others.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Author : Franco Taroni
Publisher : John Wiley & Sons
ISBN 13 : 0470979739
Total Pages : 472 pages
Book Rating : 4.4/5 (79 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science by : Franco Taroni

Download or read book Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science written by Franco Taroni and published by John Wiley & Sons. This book was released on 2014-09-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks “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 Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition 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 diffculties 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.

Foundations of Intelligent Systems

Author : Zbigniew W. Ras
Publisher : Springer
ISBN 13 : 3540399631
Total Pages : 658 pages
Book Rating : 4.5/5 (43 download)

DOWNLOAD NOW!

Book Synopsis Foundations of Intelligent Systems by : Zbigniew W. Ras

Download or read book Foundations of Intelligent Systems written by Zbigniew W. Ras and published by Springer. This book was released on 2003-07-31 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Of Testing ExperimentsConclusion; Acknowledgments; References; Can Relational Learning Scale Up?; Introduction; Phase Transition in Hypothesis Testing; Experiment Goal and Setting; Results; Interpretation; The Phase Transition Is an Attractor; Correct Identification of the Target Concept; Good Approximation of the Target Concept; Conclusion; References; Discovering Geographic Knowledge: The INGENS System; Introduction; INGENS Software Architecture and Object Data Model; Learning Classification Rules for Geographical Objects; Application to Apulian Map Interpretation.