Bayesian Artificial Intelligence

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Bayesian Artificial Intelligence, Second Edition

Kevin B. Korb,Ann E. Nicholson
Bayesian Artificial Intelligence Second Edition Book PDF

Author : Kevin B. Korb,Ann E. Nicholson
Publisher : CRC Press
Page : 491 pages
File Size : 49,8 Mb
Release : 2010-12-16
Category : Business & Economics
ISBN : 1439815917

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Bayesian Artificial Intelligence, Second Edition by Kevin B. Korb,Ann E. Nicholson Pdf

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

Bayesian Artificial Intelligence

Kevin B. Korb,Ann E. Nicholson
Bayesian Artificial Intelligence Book PDF

Author : Kevin B. Korb,Ann E. Nicholson
Publisher : CRC Press
Page : 481 pages
File Size : 51,9 Mb
Release : 2010-12-16
Category : Business & Economics
ISBN : 9781439815922

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Bayesian Artificial Intelligence by Kevin B. Korb,Ann E. Nicholson Pdf

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors dis

Bayesian Reasoning and Machine Learning

David Barber
Bayesian Reasoning and Machine Learning Book PDF

Author : David Barber
Publisher : Cambridge University Press
Page : 739 pages
File Size : 52,7 Mb
Release : 2012-02-02
Category : Computers
ISBN : 9780521518147

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Bayesian Reasoning and Machine Learning by David Barber Pdf

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Bayesian Networks

Olivier Pourret,Patrick Naïm,Bruce Marcot
Bayesian Networks Book PDF

Author : Olivier Pourret,Patrick Naïm,Bruce Marcot
Publisher : John Wiley & Sons
Page : 446 pages
File Size : 41,8 Mb
Release : 2008-04-30
Category : Mathematics
ISBN : 0470994541

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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.

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Bayesian Reasoning and Gaussian Processes for Machine Learning Applications Book PDF

Author : Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Publisher : CRC Press
Page : 165 pages
File Size : 52,7 Mb
Release : 2022-04-14
Category : Business & Economics
ISBN : 9781000569599

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Bayesian Reasoning and Gaussian Processes for Machine Learning Applications by Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose Pdf

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.

Bayesian Learning for Neural Networks

Radford M. Neal
Bayesian Learning for Neural Networks Book PDF

Author : Radford M. Neal
Publisher : Springer Science & Business Media
Page : 194 pages
File Size : 44,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461207450

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Bayesian Learning for Neural Networks by Radford M. Neal Pdf

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Advanced Lectures on Machine Learning

Olivier Bousquet,Ulrike von Luxburg,Gunnar Rätsch
Advanced Lectures on Machine Learning Book PDF

Author : Olivier Bousquet,Ulrike von Luxburg,Gunnar Rätsch
Publisher : Springer
Page : 246 pages
File Size : 52,6 Mb
Release : 2011-03-22
Category : Computers
ISBN : 9783540286509

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Advanced Lectures on Machine Learning by Olivier Bousquet,Ulrike von Luxburg,Gunnar Rätsch Pdf

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Learning from Data

Doug Fisher,Hans-J. Lenz
Learning from Data Book PDF

Author : Doug Fisher,Hans-J. Lenz
Publisher : Springer Science & Business Media
Page : 444 pages
File Size : 47,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461224044

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Learning from Data by Doug Fisher,Hans-J. Lenz Pdf

Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.

Modeling and Reasoning with Bayesian Networks

Adnan Darwiche
Modeling and Reasoning with Bayesian Networks Book PDF

Author : Adnan Darwiche
Publisher : Cambridge University Press
Page : 549 pages
File Size : 40,7 Mb
Release : 2009-04-06
Category : Computers
ISBN : 9781139478908

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Modeling and Reasoning with Bayesian Networks by Adnan Darwiche Pdf

This book is 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 treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. 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.

Innovations in Bayesian Networks

Dawn E. Holmes
Innovations in Bayesian Networks Book PDF

Author : Dawn E. Holmes
Publisher : Springer
Page : 322 pages
File Size : 51,8 Mb
Release : 2008-09-10
Category : Technology & Engineering
ISBN : 9783540850663

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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.

Uncertainty in Artificial Intelligence

L.N. Kanal,J.F. Lemmer
Uncertainty in Artificial Intelligence Book PDF

Author : L.N. Kanal,J.F. Lemmer
Publisher : Elsevier
Page : 522 pages
File Size : 55,5 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9781483296524

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Uncertainty in Artificial Intelligence by L.N. Kanal,J.F. Lemmer Pdf

How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy. Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

Statistical Relational Artificial Intelligence

Luc De Kang,Kristian Chen,Sriraam Yu,David Genesereth
Statistical Relational Artificial Intelligence Book PDF

Author : Luc De Kang,Kristian Chen,Sriraam Yu,David Genesereth
Publisher : Springer Nature
Page : 175 pages
File Size : 48,6 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015748

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Statistical Relational Artificial Intelligence by Luc De Kang,Kristian Chen,Sriraam Yu,David Genesereth Pdf

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Emerging Paradigms in Machine Learning

Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett
Emerging Paradigms in Machine Learning Book PDF

Author : Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett
Publisher : Springer Science & Business Media
Page : 498 pages
File Size : 42,7 Mb
Release : 2012-07-31
Category : Technology & Engineering
ISBN : 9783642286995

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Emerging Paradigms in Machine Learning by Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett Pdf

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.

Advances in Artificial Intelligence

Guilherme Bittencourt,Geber L. Ramalho
Advances in Artificial Intelligence Book PDF

Author : Guilherme Bittencourt,Geber L. Ramalho
Publisher : Springer
Page : 422 pages
File Size : 43,9 Mb
Release : 2003-08-02
Category : Computers
ISBN : 9783540361275

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Advances in Artificial Intelligence by Guilherme Bittencourt,Geber L. Ramalho Pdf

The biennial Brazilian Symposium on Arti?cial Intelligence (SBIA 2002) – of which this is the 16th event – is a meeting and discussion forum for arti?cial intelligence researchers and practitioners worldwide. SBIA is the leading c- ference in Brazil for the presentation of research and applications in arti?cial intelligence. The ?rst SBIA was held in 1984, and since 1995 it has been an international conference, with papers written in English and an international program committee, which this year was composed of 45 researchers from 13 countries. SBIA 2002 was held in conjunction with the VII Brazilian Symposium on Neural Networks (SBRN 2002). SBRN 2002 focuses on neural networks and on other models of computational intelligence. SBIA 2002, supported by the Brazilian Computer Society (SBC), was held in Porto de Galinhas/Recife, Brazil, 11–14 November 2002. The call for papers was very successful, resulting in 146 papers submitted from 18 countries. A total of 39 papers were accepted for publication in the proceedings. We would like to thank the SBIA 2002 sponsoring organizations, CNPq, Capes, and CESAR, and also all the authors who submitted papers. In particular, we would like to thank the program committee members and the additional referees for the di?cult task of reviewing and commenting on the submitted papers.

Learning Bayesian Networks

Richard E. Neapolitan
Learning Bayesian Networks Book PDF

Author : Richard E. Neapolitan
Publisher : Prentice Hall
Page : 704 pages
File Size : 55,9 Mb
Release : 2004
Category : Computers
ISBN : STANFORD:36105111872318

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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.