## What are Empirical Bayes Methods?

Empirical Bayes methods are a collection of ways to estimate and update the parameters of a prior probability before creating a posterior probability distribution. This technique still follows the general Bayesian statistics model, but turns the process of estimating initial assumptions (prior probability) into a two-step procedure. Empirical Bayes estimation is used instead of the Maximum Entropy Principle when more than one parameter is known, but still not enough is known to create a fixed-point probability distribution without subjective guesswork.

### How Do Empirical Bayes Methods Work?

There are different Bayes estimation techniques for each type of probability distribution, but all share the same basic format:

Step 1: Create hyperparameters (probability distributions) instead of fixed values for each parameter in a prior assumption.

Step 2: Test the prior probability on a sample of data, which turns the hyperparameters into an approximate value for each parameter.

Step 3: Use this updated prior assumption, which is technically a posterior probability, as a prior probability when running the model on the full data set.

One advantage of this approach is that you can still have a conjugate prior/posterior relationship even if the initial hyperparameters and the prior probability parameters use different distributions, since only the final outcome is used in the model.