  In this tutorial we review the essential arguments behing entropic inference.
We focus on the epistemological notion of information and its relation to the
Bayesian beliefs of rational agents. The problem of updating from a prior to a
posterior probability distribution is tackled through an eliminative induction
process that singles out the logarithmic relative entropy as the unique tool
for inference. The resulting method of Maximum relative Entropy (ME), includes
as special cases both MaxEnt and Bayes' rule, and therefore unifies the two
themes of these workshops -- the Maximum Entropy and the Bayesian methods --
into a single general inference scheme.
