

PreMoLab Seminar
March 16, 2012 17:00–17:30, Moscow, A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences (Bol'shoi Karetnyi per., 19), room 615






Bernstein  von Mises Theorem for quasiposterior
Spokoiny V.^{} 
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Abstract:
Bernstein  von Mises Theorem is one of the most remarkable result in Bayesian inference.
It claims that under rather weak conditions on the model and on the prior, the posterior distribution is
asymptotically close to a normal distribution with the mean at the MLE and the covariance matrix which is inverse
of the total Fisher information matrix.This talk extends this result to the situation when the likelihood function is possibly misspecified, the sample size is fixed and does not tend to infinity, and the parameter dimension is large relative to sample size. A further extension to hyperpriors and Bayesian model selection is discussed as well.

