Abstract Details
Activity Number:
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364
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #310356 |
Title:
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A Note on DIC Justification
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Author(s):
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Shouhao Zhou*+
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Companies:
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The University of Texas MD Anderson Cancer Center
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Keywords:
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bayesian modeling ;
model selection ;
deviance ;
DIC
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Abstract:
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DIC (Deviance Information Criterion) has been widely used in applications of Bayesian modelling. As a Bayesian adaption of AIC, it heuristically estimates the out-of-sample plug-in log-likelihood, a relative risk based on Kullback-Leibler divergence, but the estimation itself is criticized for lack of a clear theoretical foundation. In this article, I will provide a rigid justification for DIC, explore its generalization for mis-specified candidate models, and discuss the switch to a modified version of DIC with respect to Bayesian expected loss.
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Authors who are presenting talks have a * after their name.
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