Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Title Bayesian Learning for Neural Networks PDF eBook
Author Radford M. Neal
Publisher Springer Science & Business Media
Pages 194
Release 2012-12-06
Genre Mathematics
ISBN 1461207452

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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.

Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Title Bayesian Learning for Neural Networks PDF eBook
Author Radford M. Neal
Publisher Springer
Pages 0
Release 1996-08-09
Genre Mathematics
ISBN 9780387947242

Download Bayesian Learning for Neural Networks Book in PDF, Epub and Kindle

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.

Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning
Title Bayesian Reasoning and Machine Learning PDF eBook
Author David Barber
Publisher Cambridge University Press
Pages 739
Release 2012-02-02
Genre Computers
ISBN 0521518148

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

Learning Bayesian Networks

Learning Bayesian Networks
Title Learning Bayesian Networks PDF eBook
Author Richard E. Neapolitan
Publisher Prentice Hall
Pages 704
Release 2004
Genre Computers
ISBN

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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.

Advanced Lectures on Machine Learning

Advanced Lectures on Machine Learning
Title Advanced Lectures on Machine Learning PDF eBook
Author Olivier Bousquet
Publisher Springer
Pages 249
Release 2011-03-22
Genre Computers
ISBN 3540286500

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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.

Bayesian Nonparametrics via Neural Networks

Bayesian Nonparametrics via Neural Networks
Title Bayesian Nonparametrics via Neural Networks PDF eBook
Author Herbert K. H. Lee
Publisher SIAM
Pages 106
Release 2004-01-01
Genre Mathematics
ISBN 9780898718423

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Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Graphical Models, Exponential Families, and Variational Inference

Graphical Models, Exponential Families, and Variational Inference
Title Graphical Models, Exponential Families, and Variational Inference PDF eBook
Author Martin J. Wainwright
Publisher Now Publishers Inc
Pages 324
Release 2008
Genre Computers
ISBN 1601981848

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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.