Abstract Details
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Activity Number:
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617
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Type:
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Invited
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Date/Time:
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Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #307331 |
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Title:
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On Nonparametric Bernstein-Von Mises Theorems
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Author(s):
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Ismael Castillo*+
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Companies:
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CNRS
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Keywords:
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Bayesian nonparametrics ;
Bernstein-von Mises Theorems
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Abstract:
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We investigate Bernstein-von Mises Theorems in non-parametric frameworks, focusing on Gaussian white noise and density estimation. We show that under some mild conditions on the prior, the non-parametric Bayesian posterior converges weakly to a Gaussian limit, provided weak convergence is stated in a sufficiently large space. Particularly we investigate frequentist coverage properties of Bayesian credible sets. Applications include goodness-of ?t tests, as well as general classes of linear and nonlinear functionals.
This is joint work with Richard Nickl.
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Authors who are presenting talks have a * after their name.
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