Graphical Belief Modeling

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Graphical Belief Modeling

Author : Russel .G Almond
Publisher : Routledge
ISBN 13 : 1351444476
Total Pages : 432 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Graphical Belief Modeling by : Russel .G Almond

Download or read book Graphical Belief Modeling written by Russel .G Almond and published by Routledge. This book was released on 2022-01-27 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This innovative volume explores graphical models using belief functions as a representation of uncertainty, offering an alternative approach to problems where probability proves inadequate. Graphical Belief Modeling makes it easy to compare the two approaches while evaluating their relative strengths and limitations. The author examines both theory and computation, incorporating practical notes from the author's own experience with the BELIEF software package. As one of the first volumes to apply the Dempster-Shafer belief functions to a practical model, a substantial portion of the book is devoted to a single example--calculating the reliability of a complex system. This special feature enables readers to gain a thorough understanding of the application of this methodology. The first section provides a description of graphical belief models and probablistic graphical models that form an important subset: the second section discusses the algorithm used in the manipulation of graphical models: the final segment of the book offers a complete description of the risk assessment example, as well as the methodology used to describe it. Graphical Belief Modeling offers researchers and graduate students in artificial intelligence and statistics more than just a new approach to an old reliability task: it provides them with an invaluable illustration of the process of graphical belief modeling.

Graphical Belief Modeling

Author : Russel .G Almond
Publisher : CRC Press
ISBN 13 : 9780412066610
Total Pages : 458 pages
Book Rating : 4.0/5 (666 download)

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Book Synopsis Graphical Belief Modeling by : Russel .G Almond

Download or read book Graphical Belief Modeling written by Russel .G Almond and published by CRC Press. This book was released on 1995-11-30 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This innovative volume explores graphical models using belief functions as a representation of uncertainty, offering an alternative approach to problems where probability proves inadequate. Graphical Belief Modeling makes it easy to compare the two approaches while evaluating their relative strengths and limitations. The author examines both theory and computation, incorporating practical notes from the author's own experience with the BELIEF software package. As one of the first volumes to apply the Dempster-Shafer belief functions to a practical model, a substantial portion of the book is devoted to a single example--calculating the reliability of a complex system. This special feature enables readers to gain a thorough understanding of the application of this methodology. The first section provides a description of graphical belief models and probablistic graphical models that form an important subset: the second section discusses the algorithm used in the manipulation of graphical models: the final segment of the book offers a complete description of the risk assessment example, as well as the methodology used to describe it. Graphical Belief Modeling offers researchers and graduate students in artificial intelligence and statistics more than just a new approach to an old reliability task: it provides them with an invaluable illustration of the process of graphical belief modeling.

Belief Functions: Theory and Applications

Author : Thierry Denoeux
Publisher : Springer Science & Business Media
ISBN 13 : 3642294618
Total Pages : 442 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Belief Functions: Theory and Applications by : Thierry Denoeux

Download or read book Belief Functions: Theory and Applications written by Thierry Denoeux and published by Springer Science & Business Media. This book was released on 2012-04-26 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories. This volume contains the proceedings of the 2nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.

Handbook of Graphical Models

Author : Marloes Maathuis
Publisher : CRC Press
ISBN 13 : 0429874235
Total Pages : 666 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis Handbook of Graphical Models by : Marloes Maathuis

Download or read book Handbook of Graphical Models written by Marloes Maathuis and published by CRC Press. This book was released on 2018-11-12 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art. Key features: * Contributions by leading researchers from a range of disciplines * Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications * Balanced coverage of concepts, theory, methods, examples, and applications * Chapters can be read mostly independently, while cross-references highlight connections The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.

Social Capital Modeling in Virtual Communities: Bayesian Belief Network Approaches

Author : Daniel, Ben
Publisher : IGI Global
ISBN 13 : 1605666645
Total Pages : 284 pages
Book Rating : 4.6/5 (56 download)

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Book Synopsis Social Capital Modeling in Virtual Communities: Bayesian Belief Network Approaches by : Daniel, Ben

Download or read book Social Capital Modeling in Virtual Communities: Bayesian Belief Network Approaches written by Daniel, Ben and published by IGI Global. This book was released on 2009-05-31 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this book researchers have employed different approaches to examine and describe various types of relationships among people in communities by using social capital as a conceptual and theoretical tool"--Provided by publisher.

Selecting Models from Data

Author : P. Cheeseman
Publisher : Springer Science & Business Media
ISBN 13 : 1461226600
Total Pages : 475 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Selecting Models from Data by : P. Cheeseman

Download or read book Selecting Models from Data written by P. Cheeseman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

Probabilistic Graphical Models

Author : Daphne Koller
Publisher : MIT Press
ISBN 13 : 0262013193
Total Pages : 1268 pages
Book Rating : 4.2/5 (62 download)

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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 1268 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.

Codes, Systems, and Graphical Models

Author : Brian Marcus
Publisher : Springer Science & Business Media
ISBN 13 : 9780387951737
Total Pages : 540 pages
Book Rating : 4.9/5 (517 download)

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Book Synopsis Codes, Systems, and Graphical Models by : Brian Marcus

Download or read book Codes, Systems, and Graphical Models written by Brian Marcus and published by Springer Science & Business Media. This book was released on 2001-03-01 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Coding theory, system theory, and symbolic dynamics have much in common. A major new theme in this area of research is that of codes and systems based on graphical models. This volume contains survey and research articles from leading researchers at the interface of these subjects.

Mastering Probabilistic Graphical Models Using Python

Author : Ankur Ankan
Publisher : Packt Publishing Ltd
ISBN 13 : 1784395218
Total Pages : 284 pages
Book Rating : 4.7/5 (843 download)

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Book Synopsis Mastering Probabilistic Graphical Models Using Python by : Ankur Ankan

Download or read book Mastering Probabilistic Graphical Models Using Python written by Ankur Ankan and published by Packt Publishing Ltd. This book was released on 2015-08-03 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book Gain in-depth knowledge of Probabilistic Graphical Models Model time-series problems using Dynamic Bayesian Networks A practical guide to help you apply PGMs to real-world problems Who This Book Is For If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem. What You Will Learn Get to know the basics of Probability theory and Graph Theory Work with Markov Networks Implement Bayesian Networks Exact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm Understand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms Sample algorithms in Graphical Models Grasp details of Naive Bayes with real-world examples Deploy PGMs using various libraries in Python Gain working details of Hidden Markov Models with real-world examples In Detail Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples. Style and approach An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.

Graphical Models for Security

Author : Barbara Kordy
Publisher : Springer
ISBN 13 : 3319462636
Total Pages : 167 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Graphical Models for Security by : Barbara Kordy

Download or read book Graphical Models for Security written by Barbara Kordy and published by Springer. This book was released on 2016-09-07 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings from the Third International Workshop on Graphical Models for Security, GraMSec 2016, held in Lisbon, Portugal, in June 2016. The 9 papers presented in this volume were carefully reviewed and selected from 23 submissions. The volume also contains the invited talk by Xinming Ou. GraMSec contributes to the development of well-founded graphical security models, efficient algorithms for their analysis, as well as methodologies for their practical usage.

Learning in Graphical Models

Author : M.I. Jordan
Publisher : Springer Science & Business Media
ISBN 13 : 9401150141
Total Pages : 630 pages
Book Rating : 4.4/5 (11 download)

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Book Synopsis Learning in Graphical Models by : M.I. Jordan

Download or read book Learning in Graphical Models written by M.I. Jordan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Probabilistic Graphical Models for Computer Vision.

Author : Qiang Ji
Publisher : Academic Press
ISBN 13 : 0128034955
Total Pages : 322 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Probabilistic Graphical Models for Computer Vision. by : Qiang Ji

Download or read book Probabilistic Graphical Models for Computer Vision. written by Qiang Ji and published by Academic Press. This book was released on 2019-12-12 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. Discusses PGM theories and techniques with computer vision examples Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision Includes an extensive list of references, online resources and a list of publicly available and commercial software Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction

Bayesian Network Technologies: Applications and Graphical Models

Author : Mittal, Ankush
Publisher : IGI Global
ISBN 13 : 159904143X
Total Pages : 368 pages
Book Rating : 4.5/5 (99 download)

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Book Synopsis Bayesian Network Technologies: Applications and Graphical Models by : Mittal, Ankush

Download or read book Bayesian Network Technologies: Applications and Graphical Models written by Mittal, Ankush and published by IGI Global. This book was released on 2007-03-31 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an excellent, well-balanced collection of areas where Bayesian networks have been successfully applied; it describes the underlying concepts of Bayesian Networks with the help of diverse applications, and theories that prove Bayesian networks valid"--Provided by publisher.

Probabilistic Graphical Models

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

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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.

Computer Vision, Imaging and Computer Graphics. Theory and Applications

Author : Paul Richard
Publisher : Springer
ISBN 13 : 3642253822
Total Pages : 327 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Computer Vision, Imaging and Computer Graphics. Theory and Applications by : Paul Richard

Download or read book Computer Vision, Imaging and Computer Graphics. Theory and Applications written by Paul Richard and published by Springer. This book was released on 2012-02-25 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Conference, VISIGRAPP 2010, the Joint Conference on Computer Vision Theory and Applications (VISAPP), on Imaging Theory and Applications (IMAGAPP), and on Computer Graphics Theory and Applications (GRAPP), held in Angers, France, in May 2010. The 19 revised full papers presented together with two invited papers were carefully reviewed and selected. The papers are organized in topical sections on computer vision theory and applications; imaging theory and applications; computer graphics theory and applications; and information visualization theory and applications.

Belief Functions: Theory and Applications

Author : Sylvie Le Hégarat-Mascle
Publisher : Springer Nature
ISBN 13 : 3031178017
Total Pages : 318 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Belief Functions: Theory and Applications by : Sylvie Le Hégarat-Mascle

Download or read book Belief Functions: Theory and Applications written by Sylvie Le Hégarat-Mascle and published by Springer Nature. This book was released on 2022-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition. The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.

A Guided Tour of Artificial Intelligence Research

Author : Pierre Marquis
Publisher : Springer Nature
ISBN 13 : 3030061671
Total Pages : 529 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis A Guided Tour of Artificial Intelligence Research by : Pierre Marquis

Download or read book A Guided Tour of Artificial Intelligence Research written by Pierre Marquis and published by Springer Nature. This book was released on 2020-05-08 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This second volume presents the main families of algorithms developed or used in AI to learn, to infer, to decide. Generic approaches to problem solving are presented: ordered heuristic search, as well as metaheuristics are considered. Algorithms for processing logic-based representations of various types (first-order formulae, propositional formulae, logic programs, etc.) and graphical models of various types (standard constraint networks, valued ones, Bayes nets, Markov random fields, etc.) are presented. The volume also focuses on algorithms which have been developed to simulate specific ‘intelligent” processes such as planning, playing, learning, and extracting knowledge from data. Finally, an afterword draws a parallel between algorithmic problems in operation research and in AI.