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 language | English |
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Pages (from-to) | 15-19 |
Number of pages | 5 |
Journal | Journal of Applied Probability and Statistics |
Volume | 10 |
Issue number | 1 |
State | Published - 1 Jan 2015 |
Keywords
- Conjugate prior
- Convergence to a point mass
- Empirical Bayes
- Maximum likelihood estimator