Abstract Details
Activity Number:
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450
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Type:
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Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 3:05 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #317856
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Title:
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Robust Bayesian Inference via Coarsening
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Author(s):
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Jeffrey Miller* and David Dunson
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Companies:
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Duke University and Duke University
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Keywords:
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Model misspecification ;
Robustness ;
Tempering ;
Mixture model ;
Relative entropy ;
Bayesian
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Abstract:
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The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a simple but coherent approach to Bayesian inference that improves robustness to perturbations of the model: rather than condition on the data exactly, one conditions on a neighborhood of the empirical distribution. In certain cases, inference is easily implemented using standard methods. We illustrate with real and simulated data, and provide theoretical results.
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Authors who are presenting talks have a * after their name.
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