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Graphical Belief Modeling

Russel .G Almond

Author : Russel .G Almond
Publisher : Routledge
Page : 432 pages
File Size : 47,6 Mb
Release : 2022-01-27
Category : Mathematics
ISBN : 9781351444477

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Graphical Belief Modeling by Russel .G Almond Pdf

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

Russel .G Almond

Author : Russel .G Almond
Publisher : CRC Press
Page : 458 pages
File Size : 52,6 Mb
Release : 1995-11-30
Category : Mathematics
ISBN : 0412066610

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Graphical Belief Modeling by Russel .G Almond Pdf

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.

Probabilistic Graphical Models

Daphne Koller,Nir Friedman

Author : Daphne Koller,Nir Friedman
Publisher : MIT Press
Page : 1270 pages
File Size : 41,5 Mb
Release : 2009-07-31
Category : Computers
ISBN : 9780262258357

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Probabilistic Graphical Models by Daphne Koller,Nir Friedman Pdf

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.

Belief Functions: Theory and Applications

Thierry Denoeux,Marie-Hélène Masson

Author : Thierry Denoeux,Marie-Hélène Masson
Publisher : Springer Science & Business Media
Page : 442 pages
File Size : 51,9 Mb
Release : 2012-04-26
Category : Technology & Engineering
ISBN : 9783642294617

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Belief Functions: Theory and Applications by Thierry Denoeux,Marie-Hélène Masson Pdf

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.

Graphical Models, Exponential Families, and Variational Inference

Martin J. Wainwright,Michael Irwin Jordan

Author : Martin J. Wainwright,Michael Irwin Jordan
Publisher : Now Publishers Inc
Page : 324 pages
File Size : 48,5 Mb
Release : 2008
Category : Computers
ISBN : 9781601981844

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Graphical Models, Exponential Families, and Variational Inference by Martin J. Wainwright,Michael Irwin Jordan Pdf

The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.

Handbook of Graphical Models

Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright

Author : Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright
Publisher : CRC Press
Page : 666 pages
File Size : 49,5 Mb
Release : 2018-11-12
Category : Mathematics
ISBN : 9780429874239

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Handbook of Graphical Models by Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright Pdf

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

Daniel, Ben

Author : Daniel, Ben
Publisher : IGI Global
Page : 284 pages
File Size : 48,8 Mb
Release : 2009-05-31
Category : Education
ISBN : 9781605666648

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

"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

P. Cheeseman,R.W. Oldford

Author : P. Cheeseman,R.W. Oldford
Publisher : Springer Science & Business Media
Page : 475 pages
File Size : 50,6 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461226604

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Selecting Models from Data by P. Cheeseman,R.W. Oldford Pdf

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.

Learning in Graphical Models

M.I. Jordan

Author : M.I. Jordan
Publisher : Springer Science & Business Media
Page : 658 pages
File Size : 41,6 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.

Handbook of Graphical Models

Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright

Author : Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright
Publisher : CRC Press
Page : 536 pages
File Size : 54,8 Mb
Release : 2018-11-12
Category : Mathematics
ISBN : 9780429874246

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Handbook of Graphical Models by Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright Pdf

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.

Probabilistic Graphical Models

Daphne Koller,Nir Friedman

Author : Daphne Koller,Nir Friedman
Publisher : MIT Press
Page : 1268 pages
File Size : 47,7 Mb
Release : 2009-07-31
Category : Computers
ISBN : 9780262013192

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Probabilistic Graphical Models by Daphne Koller,Nir Friedman Pdf

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.

Mastering Probabilistic Graphical Models Using Python

Ankur Ankan,Abinash Panda

Author : Ankur Ankan,Abinash Panda
Publisher : Packt Publishing Ltd
Page : 284 pages
File Size : 45,5 Mb
Release : 2015-08-03
Category : Computers
ISBN : 9781784395216

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Mastering Probabilistic Graphical Models Using Python by Ankur Ankan,Abinash Panda Pdf

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

Barbara Kordy,Mathias Ekstedt,Dong Seong Kim

Author : Barbara Kordy,Mathias Ekstedt,Dong Seong Kim
Publisher : Springer
Page : 167 pages
File Size : 44,9 Mb
Release : 2016-09-07
Category : Computers
ISBN : 9783319462639

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Graphical Models for Security by Barbara Kordy,Mathias Ekstedt,Dong Seong Kim Pdf

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.

Codes, Systems, and Graphical Models

Brian Marcus,Joachim Rosenthal

Author : Brian Marcus,Joachim Rosenthal
Publisher : Springer Science & Business Media
Page : 540 pages
File Size : 48,9 Mb
Release : 2001-03-01
Category : Computers
ISBN : 0387951733

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Codes, Systems, and Graphical Models by Brian Marcus,Joachim Rosenthal Pdf

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.

Probabilistic Graphical Models for Computer Vision.

Qiang Ji

Author : Qiang Ji
Publisher : Academic Press
Page : 322 pages
File Size : 52,5 Mb
Release : 2019-12-12
Category : Technology & Engineering
ISBN : 9780128034958

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Probabilistic Graphical Models for Computer Vision. by Qiang Ji Pdf

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

Graphical Belief Modeling

Graphical Belief Modeling by Russel .G Almond Pdf

Graphical Belief Modeling by Russel .G Almond Pdf

Probabilistic Graphical Models by Daphne Koller,Nir Friedman Pdf

Belief Functions: Theory and Applications by Thierry Denoeux,Marie-Hélène Masson Pdf

Graphical Models, Exponential Families, and Variational Inference by Martin J. Wainwright,Michael Irwin Jordan Pdf

Handbook of Graphical Models by Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright Pdf

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

Selecting Models from Data by P. Cheeseman,R.W. Oldford Pdf

Learning in Graphical Models by M.I. Jordan Pdf

Handbook of Graphical Models by Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright Pdf

Probabilistic Graphical Models by Daphne Koller,Nir Friedman Pdf

Mastering Probabilistic Graphical Models Using Python by Ankur Ankan,Abinash Panda Pdf

Graphical Models for Security by Barbara Kordy,Mathias Ekstedt,Dong Seong Kim Pdf

Codes, Systems, and Graphical Models by Brian Marcus,Joachim Rosenthal Pdf

Probabilistic Graphical Models for Computer Vision. by Qiang Ji Pdf

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