Advances In Bayesian Networks

Author : Joe Suzuki,Maomi Ueno
Publisher : Springer
Page : 281 pages
File Size : 43,5 Mb
Release : 2016-01-07
Category : Computers
ISBN : 9783319283791

Get Book

Advanced Methodologies for Bayesian Networks by Joe Suzuki,Maomi Ueno Pdf

This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015. The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.

Author : Jose A. Gámez,Serafin Moral,Antonio Salmerón Cerdan
Publisher : Springer
Page : 328 pages
File Size : 41,6 Mb
Release : 2014-03-12
Category : Mathematics
ISBN : 3642535518

Get Book

Advances in Bayesian Networks by Jose A. Gámez,Serafin Moral,Antonio Salmerón Cerdan Pdf

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Author : Douglas McNair
Publisher : Unknown
Page : 138 pages
File Size : 47,5 Mb
Release : 2019-11-06
Category : Electronic
ISBN : 9781839623226

Get Book

Bayesian Networks by Douglas McNair Pdf

Author : Adnan Darwiche
Publisher : Cambridge University Press
Page : 561 pages
File Size : 52,9 Mb
Release : 2009-04-06
Category : Computers
ISBN : 9780521884389

Get Book

Modeling and Reasoning with Bayesian Networks by Adnan Darwiche Pdf

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

Author : José A. Gámez,Serafin Moral,Antonio Salmerón Cerdan
Publisher : Springer
Page : 328 pages
File Size : 48,5 Mb
Release : 2004-02-23
Category : Mathematics
ISBN : 3540208763

Get Book

Advances in Bayesian Networks by José A. Gámez,Serafin Moral,Antonio Salmerón Cerdan Pdf

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Author : Monidipa Das,Soumya K. Ghosh
Publisher : Springer Nature
Page : 149 pages
File Size : 49,9 Mb
Release : 2019-11-07
Category : Technology & Engineering
ISBN : 9783030277499

Get Book

Enhanced Bayesian Network Models for Spatial Time Series Prediction by Monidipa Das,Soumya K. Ghosh Pdf

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Author : Thomas Dyhre Nielsen,FINN VERNER JENSEN
Publisher : Springer Science & Business Media
Page : 457 pages
File Size : 43,9 Mb
Release : 2009-03-17
Category : Science
ISBN : 9780387682822

Get Book

Bayesian Networks and Decision Graphs by Thomas Dyhre Nielsen,FINN VERNER JENSEN Pdf

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.

Author : Olivier Pourret,Patrick Naïm,Bruce Marcot
Publisher : John Wiley & Sons
Page : 446 pages
File Size : 44,5 Mb
Release : 2008-04-30
Category : Mathematics
ISBN : 0470994541

Get Book

Bayesian Networks by Olivier Pourret,Patrick Naïm,Bruce Marcot Pdf

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.

Author : José A. Gámez,Serafin Moral,Antonio Salmerón Cerdan
Publisher : Springer
Page : 334 pages
File Size : 53,5 Mb
Release : 2013-06-29
Category : Mathematics
ISBN : 9783540398790

Get Book

Advances in Bayesian Networks by José A. Gámez,Serafin Moral,Antonio Salmerón Cerdan Pdf

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Author : Baoping Cai,Yonghong Liu,Zengkai Liu,Yuanjiang Chang,Lei Jiang
Publisher : Springer
Page : 257 pages
File Size : 45,9 Mb
Release : 2019-02-28
Category : Technology & Engineering
ISBN : 9789811365164

Get Book

Bayesian Networks for Reliability Engineering by Baoping Cai,Yonghong Liu,Zengkai Liu,Yuanjiang Chang,Lei Jiang Pdf

This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.

Author : Dawn E. Holmes
Publisher : Springer Science & Business Media
Page : 324 pages
File Size : 40,5 Mb
Release : 2008-10-02
Category : Mathematics
ISBN : 9783540850656

Get Book

Innovations in Bayesian Networks by Dawn E. Holmes Pdf

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.

Author : Finn V. Jensen
Publisher : Springer
Page : 178 pages
File Size : 54,8 Mb
Release : 1997-08-15
Category : Mathematics
ISBN : 0387915028

Get Book

Introduction to Bayesian Networks by Finn V. Jensen Pdf

Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.

Author : Timo Koski,John Noble
Publisher : Wiley
Page : 366 pages
File Size : 46,6 Mb
Release : 2009-09-24
Category : Mathematics
ISBN : 9780470684030

Get Book

Bayesian Networks by Timo Koski,John Noble Pdf

Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications. A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.

Author : Russell G. Almond,Robert J. Mislevy,Linda S. Steinberg,Duanli Yan,David M. Williamson
Publisher : Springer
Page : 662 pages
File Size : 43,9 Mb
Release : 2015-03-10
Category : Social Science
ISBN : 9781493921256

Get Book

Bayesian Networks in Educational Assessment by Russell G. Almond,Robert J. Mislevy,Linda S. Steinberg,Duanli Yan,David M. Williamson Pdf

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.

Author : Richard E. Neapolitan
Publisher : Prentice Hall
Page : 704 pages
File Size : 54,8 Mb
Release : 2004
Category : Computers
ISBN : STANFORD:36105111872318

Get Book

Learning Bayesian Networks by Richard E. Neapolitan Pdf

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.