Technion, IEM faculty - Statistics Seminar
Speaker: Ya`acov Ritov, Hebrew University
Title: Bayesian illusions
Date: 13/05/2012
Time: 14:30
Place: Bloomfield-527
And also here:
We consider the Bayesian analysis of a few complex,
high-dimensional models, and show that intuitive priors, which
are not tailored to the fine details of the data model and the
estimated parameters, are going to fail in situations in which
simple good frequentist estimators exit.  The models we
consider are, partially observed sample, the partial linear
model, estimating linear and quadratic functionals of a white
noise models, and estimating with stopping times. We argue
that these findings do not contradict a strong version of
Doob's consistency theorem which claims that the existence of
a uniformly $\sqrt n$ consistent estimator ensures that the
Bayes posterior is $\sqrt n$ consistent for values of the
parameter with prior probability 1.
Technion Math. Net (TECHMATH)
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