Empirical Bayes procedures can be shown to be robust to the distribution of the data in a way that Bayesian procedures cannot. The difference between Empirical bayes and Bayesianism along this important dimension make them very distinct procedures from the perspective of many users.
This difference is most commonly seen in practice when some density must be estimated for inference. Use of kernel density estimation in empirical Bayes ensures an asymptotic convergence to the true density at some rate. In contrast, no Bayesian prior has yet been developed with consistency for density estimation.
Empirical Bayes procedures can be shown to be robust to the distribution of the data in a way that Bayesian procedures cannot. The difference between Empirical bayes and Bayesianism along this important dimension make them very distinct procedures from the perspective of many users.
This difference is most commonly seen in practice when some density must be estimated for inference. Use of kernel density estimation in empirical Bayes ensures an asymptotic convergence to the true density at some rate. In contrast, no Bayesian prior has yet been developed with consistency for density estimation.