  The method of Maximum (relative) Entropy (ME) is used to translate the
information contained in the known form of the likelihood into a prior
distribution for Bayesian inference. The argument is guided by intuition gained
from the successful use of ME methods in statistical mechanics. For experiments
that cannot be repeated the resulting "entropic prior" is formally identical
with the Einstein fluctuation formula. For repeatable experiments, however, the
expected value of the entropy of the likelihood turns out to be relevant
information that must be included in the analysis. As an example the entropic
prior for a Gaussian likelihood is calculated.
