Benefits Of Bayesian Network Models

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Benefits of Bayesian Network Models

Author : Philippe Weber
Publisher : John Wiley & Sons
ISBN 13 : 184821992X
Total Pages : 146 pages
Book Rating : 4.8/5 (482 download)

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Book Synopsis Benefits of Bayesian Network Models by : Philippe Weber

Download or read book Benefits of Bayesian Network Models written by Philippe Weber and published by John Wiley & Sons. This book was released on 2016-08-29 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.

Bayesian Networks

Author : Olivier Pourret
Publisher : John Wiley & Sons
ISBN 13 : 9780470994542
Total Pages : 446 pages
Book Rating : 4.9/5 (945 download)

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Book Synopsis Bayesian Networks by : Olivier Pourret

Download or read book Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

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)

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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.

Benefits of Bayesian Network Models

Author : Philippe Weber
Publisher : John Wiley & Sons
ISBN 13 : 1119347459
Total Pages : 146 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Benefits of Bayesian Network Models by : Philippe Weber

Download or read book Benefits of Bayesian Network Models written by Philippe Weber and published by John Wiley & Sons. This book was released on 2016-08-23 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.

Doing Meta-Analysis with R

Author : Mathias Harrer
Publisher : CRC Press
ISBN 13 : 1000435636
Total Pages : 500 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Doing Meta-Analysis with R by : Mathias Harrer

Download or read book Doing Meta-Analysis with R written by Mathias Harrer and published by CRC Press. This book was released on 2021-09-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Bayesian Networks in Educational Assessment

Author : Russell G. Almond
Publisher : Springer
ISBN 13 : 1493921258
Total Pages : 662 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Bayesian Networks in Educational Assessment by : Russell G. Almond

Download or read book Bayesian Networks in Educational Assessment written by Russell G. Almond and published by Springer. This book was released on 2015-03-10 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.

Bayesian Networks

Author : Marco Scutari
Publisher : CRC Press
ISBN 13 : 1482225581
Total Pages : 243 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Bayesian Networks by : Marco Scutari

Download or read book Bayesian Networks written by Marco Scutari and published by CRC Press. This book was released on 2014-06-20 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.

Innovations in Bayesian Networks

Author : Dawn E. Holmes
Publisher : Springer Science & Business Media
ISBN 13 : 3540850651
Total Pages : 324 pages
Book Rating : 4.5/5 (48 download)

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Book Synopsis Innovations in Bayesian Networks by : Dawn E. Holmes

Download or read book Innovations in Bayesian Networks written by Dawn E. Holmes and published by Springer Science & Business Media. This book was released on 2008-10-02 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.

Social Computing, Behavioral-Cultural Modeling and Prediction

Author : John Salerno
Publisher : Springer
ISBN 13 : 364219656X
Total Pages : 384 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Social Computing, Behavioral-Cultural Modeling and Prediction by : John Salerno

Download or read book Social Computing, Behavioral-Cultural Modeling and Prediction written by John Salerno and published by Springer. This book was released on 2011-02-21 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, held in College Park, MD, USA, March 29-31, 2011. The 48 papers and 3 keynotes presented in this volume were carefully reviewed and selected from 88 submissions. The papers cover a wide range of topics including social network analysis; modeling; machine learning and data mining; social behaviors; public health; cultural aspects; and effects and search.

Learning Bayesian Networks

Author : Richard E. Neapolitan
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 704 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Learning Bayesian Networks by : Richard E. Neapolitan

Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.

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

Author : Uffe B. Kjærulff
Publisher : Springer Science & Business Media
ISBN 13 : 1461451043
Total Pages : 388 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis by : Uffe B. Kjærulff

Download or read book Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis written by Uffe B. Kjærulff and published by Springer Science & Business Media. This book was released on 2012-11-30 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Networks

Author : Wichian Premchaiswadi
Publisher :
ISBN 13 : 9789535149972
Total Pages : 126 pages
Book Rating : 4.1/5 (499 download)

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Book Synopsis Bayesian Networks by : Wichian Premchaiswadi

Download or read book Bayesian Networks written by Wichian Premchaiswadi and published by . This book was released on 2012 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. First, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Second, a Bayesian network can be used to learn causal relationships, and hence can be used to gain an understanding about a problem domain and to predict the consequences of intervention. Third, because the model has both causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in a causal form) and data. Fourth, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach to avoid the over fitting of data.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

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.

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.

Introduction to Bayesian Networks

Author : Finn V. Jensen
Publisher : Springer
ISBN 13 : 9780387915029
Total Pages : 178 pages
Book Rating : 4.9/5 (15 download)

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Book Synopsis Introduction to Bayesian Networks by : Finn V. Jensen

Download or read book Introduction to Bayesian Networks written by Finn V. Jensen and published by Springer. This book was released on 1997-08-15 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.

Bayesian Networks Handbook

Author : Mick Benson
Publisher :
ISBN 13 : 9781632400758
Total Pages : 0 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Bayesian Networks Handbook by : Mick Benson

Download or read book Bayesian Networks Handbook written by Mick Benson and published by . This book was released on 2015-02-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian network is also known as a Bayes network, belief network or causal probabilistic network. Bayesian belief networks are effective tools to incorporate different information sources with varying levels of uncertainty in a mathematically secure and calculatively effective way. A Bayesian network is a graphical model that ciphers probabilistic relationships among variables of interest. This graphical paradigm has a few significant advantages: firstly, due to the dependencies among all the variables, missing nodes data is also compensated; secondly, belief network sets up the simple relationships and it is easier to identify problematic areas and consequences; thirdly, it has both casual and probabilistic semantics; and lastly, this method along with statistical method provides efficient and balanced approach to avoid over fitting of data. This book analytically and comprehensively describes various aspects of Bayesian networks which will be of great help to students, researchers and professionals in various fields which utilize applications of this model system.