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Bayesian Learning for Neural Networks

  • Radford M. Neal

Part of the Lecture Notes in Statistics book series (LNS, volume 118)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Radford M. Neal
    Pages 1-28
  3. Radford M. Neal
    Pages 29-53
  4. Radford M. Neal
    Pages 55-98
  5. Radford M. Neal
    Pages 99-143
  6. Radford M. Neal
    Pages 145-152
  7. Back Matter
    Pages 153-185

About this book

Introduction

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.

Keywords

Fitting Likelihood algorithms artificial intelligence classification intelligence learning statistics

Authors and affiliations

  • Radford M. Neal
    • 1
  1. 1.Department of Statistics and Department of Computer ScienceUniversity of TorontoTorontoCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-0745-0
  • Copyright Information Springer-Verlag New York, Inc. 1996
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94724-2
  • Online ISBN 978-1-4612-0745-0
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site