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The Resource Bayesian nonparametrics via neural networks, Herbert K.H. Lee, (electronic resource)

Bayesian nonparametrics via neural networks, Herbert K.H. Lee, (electronic resource)

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Summary
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
Language
eng
Work
Publication
Extent
1 electronic text (x, 96 p.)
Contents
  • Preface
  • Chapter 1: Introduction
  • Chapter 2: Nonparametric Models
  • Chapter 3: Priors for Neural Networks
  • Chapter 4: Building A Model
  • Chapter 5: Conclusions
  • Appendix A: Reference Prior Derivation
  • Glossary
  • Bibliography
  • Index
Isbn
9780898718423
Link
http://libproxy.rpi.edu/login?url=http://epubs.siam.org/ebooks/siam/asa-siam_series_on_statistics_and_applied_probability/sa13
Instance
Label
Bayesian nonparametrics via neural networks
Title
Bayesian nonparametrics via neural networks
Statement of responsibility
Herbert K.H. Lee
Creator
Contributor
Subject
Language
eng
Summary
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
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Additional physical form
Also available in print version.
Cataloging source
CaBNVSL
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
ASA-SIAM series on statistics and applied probability
Series volume
13
Target audience
adult
Bayesian nonparametrics via neural networks, Herbert K.H. Lee, (electronic resource)
Label
Bayesian nonparametrics via neural networks, Herbert K.H. Lee, (electronic resource)
Link
http://libproxy.rpi.edu/login?url=http://epubs.siam.org/ebooks/siam/asa-siam_series_on_statistics_and_applied_probability/sa13
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Bibliography note
Includes bibliographical references (p. 87-94) and index
Color
black and white
Contents
Preface -- Chapter 1: Introduction -- Chapter 2: Nonparametric Models -- Chapter 3: Priors for Neural Networks -- Chapter 4: Building A Model -- Chapter 5: Conclusions -- Appendix A: Reference Prior Derivation -- Glossary -- Bibliography -- Index
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9780898718423&userID=ebsco-test&password=ebsco-test
Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b3018503'}
Extent
1 electronic text (x, 96 p.)
File format
multiple file formats
Form of item
online
Governing access note
Restricted to subscribers or individual electronic text purchasers
Isbn
9780898718423
Isbn Type
(electronic bk.)
Other physical details
ill., digital file.
Reformatting quality
access
Specific material designation
remote
System details
  • Mode of access: World Wide Web
  • System requirements: Adobe Acrobat Reader

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