The Resource Bayesian nonparametrics via neural networks, Herbert K.H. Lee, (electronic resource)
Bayesian nonparametrics via neural networks, Herbert K.H. Lee, (electronic resource)
 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 realworld problems
 Language
 eng
 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
 Label
 Bayesian nonparametrics via neural networks
 Title
 Bayesian nonparametrics via neural networks
 Statement of responsibility
 Herbert K.H. Lee
 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 realworld problems
 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
 ASASIAM series on statistics and applied probability
 Series volume
 13
 Target audience
 adult
 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/asasiam_series_on_statistics_and_applied_probability/sa13
 Bibliography note
 Includes bibliographical references (p. 8794) 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=ebscotest&password=ebscotest
 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

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