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Bayesian Inference for Partially Identified Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) - Softcover
- PublisherRoutledge
- Publication date2020
- ISBN 10 036757053X
- ISBN 13 9780367570538
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages196
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Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data
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Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data
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Paperback. Condition: Brand New. 196 pages. 9.21x6.14x0.55 inches. In Stock. Seller Inventory # __036757053X
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Bayesian Inference for Partially Identified Models
Published by
Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 036757053X
ISBN 13: 9780367570538
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs.The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification.This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide. 196 pp. Englisch. Seller Inventory # 9780367570538
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Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data
Published by
Taylor & Francis Ltd, 2020
ISBN 10: 036757053X
ISBN 13: 9780367570538
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Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data
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Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data
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Bayesian Inference for Partially Identified Models : Exploring the Limits of Limited Data
Published by
Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 036757053X
ISBN 13: 9780367570538
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs.The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification.This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide. Seller Inventory # 9780367570538
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Bayesian Inference for Partially Identified Models
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Paul Gustafson is a professor in the Department of Statistics at the University of British Columbia. He is the statistics editor for Epidemiology as well as an associate editor for the Journal of the American Statistical Associ. Seller Inventory # 594589783
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