The Annals of Statistics

The Bernstein–von Mises theorem and nonregular models

Natalia A. Bochkina and Peter J. Green

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We study the asymptotic behaviour of the posterior distribution in a broad class of statistical models where the “true” solution occurs on the boundary of the parameter space. We show that in this case Bayesian inference is consistent, and that the posterior distribution has not only Gaussian components as in the case of regular models (the Bernstein–von Mises theorem) but also has Gamma distribution components whose form depends on the behaviour of the prior distribution near the boundary and have a faster rate of convergence. We also demonstrate a remarkable property of Bayesian inference, that for some models, there appears to be no bound on efficiency of estimating the unknown parameter if it is on the boundary of the parameter space. We illustrate the results on a problem from emission tomography.

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Ann. Statist., Volume 42, Number 5 (2014), 1850-1878.

First available in Project Euclid: 11 September 2014

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Zentralblatt MATH identifier

Primary: 62F12: Asymptotic properties of estimators 62F15: Bayesian inference

Approximate posterior Bayesian inference Bernstein–von Mises theorem boundary nonregular posterior concentration SPECT tomography total variation distance variance estimation in mixed models


Bochkina, Natalia A.; Green, Peter J. The Bernstein–von Mises theorem and nonregular models. Ann. Statist. 42 (2014), no. 5, 1850--1878. doi:10.1214/14-AOS1239.

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