Bernstein–von Mises theorem

Part of a series on Statistics
Bayesian statistics
Theory
Admissible decision rule Bayesian efficiency Bayesian probability Probability interpretations Bayes' theorem Bayes factor Bayesian inference Bayesian network Prior Posterior Likelihood Conjugate prior Posterior predictive Hyperparameter Hyperprior Principle of indifference Principle of maximum entropy Empirical Bayes method Cromwell's rule Bernstein–von Mises theorem Schwarz criterion Credible interval Maximum a posteriori estimation Radical probabilism
Techniques
Bayesian linear regression Bayesian estimator Approximate Bayesian computation Markov chain Monte Carlo
Statistics portal

In Bayesian inference, the Bernstein–von Mises theorem provides the basis for the important result that the posterior distribution for unknown quantities in any problem is effectively asymptotically independent of the prior distribution (assuming it obeys Cromwell's rule) as the data sample grows large.^[1]

History

The theorem is named after Richard von Mises and S. N. Bernstein although the first proper proof was given by Joseph L. Doob in 1949 for random variables with finite probability space.^[2] Later Lucien Le Cam, his PhD student Lorraine Schwartz, David A. Freedman and Persi Diaconis extended the proof under more general assumptions.

Limitations

A remarkable result was found by Freedman in 1965: the Bernstein–von Mises theorem does not hold almost surely if the random variable has an infinite countable probability space; however this depends on allowing a very broad range of possible priors. In practice, the priors used typically in research do have the desirable property even with an infinite countable probability space.

Different summary statistics such as the mode and mean may behave differently in the posterior distribution. In Freedman's examples, the posterior density and its mean, can converge on the wrong result, but the posterior mode is consistent and will converge on the correct result.

Quotations

The statistician A. W. F. Edwards has remarked, "It is sometimes said, in defence of the Bayesian concept, that the choice of prior distribution is unimportant in practice, because it hardly influences the posterior distribution at all when there are moderate amounts of data. The less said about this 'defence' the better."^[3] This criticism does not apply to the posterior mode.

Notes

References

• Vaart, A.W. van der (1998). "10.2 Bernstein–von Mises Theorem". Asymptotic Statistics. Cambridge University Press. ISBN 0-521-49603-9.
• Doob, Joseph L. (1949), Application of the theory of martingales. Colloq. Intern. du C.N.R.S (Paris), No. 13, pp. 23–27.
• Freedman, David A. (1963). On the asymptotic behaviour of Bayes estimates in the discrete case I. The Annals of Mathematical Statistics, vol. 34, pp. 1386–1403.
• Freedman, David A. (1965). On the asymptotic behaviour of Bayes estimates in the discrete case II. The Annals of Mathematical Statistics, vol. 36, pp. 454–456.
• Le Cam, Lucien (1986). Asymptotic Methods in Statistical Decision Theory, Springer. ISBN 0-387-96307-3 (Pages 336 and 618–621).
• Lorraine Schwartz (1965). On Bayes procedures. Z. Wahrscheinlichkeitstheorie, No. 4, pp. 10–26.