The Annals of Statistics

Wald Lecture: On the Bernstein-von Mises theorem with infinite-dimensional parameters

David Freedman

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If there are many independent, identically distributed observations governed by a smooth, finite-dimensional statistical model, the Bayes estimate and the maximum likelihood estimate will be close. Furthermore, the posterior distribution of the parameter vector around the posterior mean will be close to the distribution of the maximum likelihood estimate around truth. Thus, Bayesian confidence sets have good frequentist coverage properties, and conversely. However, even for the simplest infinite-dimensional models, such results do not hold. The object here is to give some examples.

Article information

Ann. Statist. Volume 27, Number 4 (1999), 1119-1141.

First available in Project Euclid: 4 April 2002

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62A15
Secondary: 62C15: Admissibility

Asymptotic confidence sets Bayesian inference consistency Gaussian priors


Freedman, David. Wald Lecture: On the Bernstein-von Mises theorem with infinite-dimensional parameters. Ann. Statist. 27 (1999), no. 4, 1119--1141. doi:10.1214/aos/1017938917.

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