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Bayesian Evaluation of Informative Hypotheses

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Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Bayesian Evaluation of Informative Hypotheses
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    Chapter 2 Illustrative Psychological Data and Hypotheses for Bayesian Inequality Constrained Analysis of Variance
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    Chapter 3 Bayesian Estimation for Inequality Constrained Analysis of Variance
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    Chapter 4 Encompassing Prior Based Model Selection for Inequality Constrained Analysis of Variance
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    Chapter 5 An Evaluation of Bayesian Inequality Constrained Analysis of Variance
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    Chapter 6 Bayes Factors Based on Test Statistics Under Order Restrictions
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    Chapter 7 Objective Bayes Factors for Informative Hypotheses: “Completing” the Informative Hypothesis and “Splitting” the Bayes Factors
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    Chapter 8 The Bayes Factor Versus Other Model Selection Criteria for the Selection of Constrained Models
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    Chapter 9 Bayesian Versus Frequentist Inference
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    Chapter 10 Inequality Constrained Analysis of Covariance
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    Chapter 11 Inequality Constrained Latent Class Models
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    Chapter 12 Inequality Constrained Contingency Table Analysis
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    Chapter 13 Inequality Constrained Multilevel Models
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    Chapter 14 A Psychologist’s View on Bayesian Evaluation of Informative Hypotheses
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    Chapter 15 A Statistician’s View on Bayesian Evaluation of Informative Hypotheses
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    Chapter 16 A Philosopher’s View on Bayesian Evaluation of Informative Hypotheses
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Title
Bayesian Evaluation of Informative Hypotheses
Published by
Springer New York, October 2008
DOI 10.1007/978-0-387-09612-4
ISBNs
978-0-387-09611-7, 978-0-387-09612-4
Editors

Hoijtink, Herbert, Klugkist, Irene, Boelen, Paul A.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 36%
Student > Master 4 16%
Other 2 8%
Student > Bachelor 2 8%
Student > Doctoral Student 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Psychology 9 36%
Neuroscience 2 8%
Social Sciences 2 8%
Arts and Humanities 1 4%
Economics, Econometrics and Finance 1 4%
Other 6 24%
Unknown 4 16%