A connection between Bayesian and frequentist estimates

Shande Chen

Research output: Contribution to journalArticlepeer-review

Abstract

For finite sample size, the traditional Bayes estimator depends on the choice of the prior. On the other hand, a frequentist estimator, such as MLE does not use any prior for the parameter(s). In general, a Bayes estimator and a frequentist estimator could be quite different when the sample size is not large. In this article, we provide results showing that the MLE can be obtained as a limiting Bayes estimator by keeping updating the prior. The convergence is independent of the choice of the prior. Some examples using conjugate priors are also provided.

Original languageEnglish
Pages (from-to)15-19
Number of pages5
JournalJournal of Applied Probability and Statistics
Volume10
Issue number1
StatePublished - 1 Jan 2015

Keywords

  • Conjugate prior
  • Convergence to a point mass
  • Empirical Bayes
  • Maximum likelihood estimator

Fingerprint

Dive into the research topics of 'A connection between Bayesian and frequentist estimates'. Together they form a unique fingerprint.

Cite this