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Bayesian Inference for Partially Identified Models /
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...
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Chapman and Hall/CRC,
2015.
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| Edición: | 1st edition. |
| Colección: | Monographs on statistics and applied probability (Series) ;
141. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Gustafson, Paul, |e author. | |
| 245 | 1 | 0 | |a Bayesian Inference for Partially Identified Models / |c Gustafson, Paul. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a [Place of publication not identified] : |b Chapman and Hall/CRC, |c 2015. | |
| 300 | |a 1 online resource (196 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Monographs on Statistics & Applied Probability ; |v 141 | |
| 520 | |a 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. | ||
| 542 | |f Copyright © Chapman and Hall/CRC 2015 |g 2015 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | 0 | |a Online resource; Title from title page (viewed April 1, 2015). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 710 | 2 | |a O'Reilly for Higher Education (Firm), |e distributor. | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. | |
| 776 | 0 | 8 | |i Print version: |a Gustafson, Paul, 1968- |t Bayesian inference for partially identified models. |d Boca Raton, FL : CRC Press, Taylor & Francis Group, [2015] |z 9781439869390 |w (OCoLC)910942866 |
| 830 | 0 | |a Monographs on statistics and applied probability (Series) ; |v 141. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781439869406/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||