  What is information? Is it physical? We argue that in a Bayesian theory the
notion of information must be defined in terms of its effects on the beliefs of
rational agents. Information is whatever constrains rational beliefs and
therefore it is the force that induces us to change our minds. This 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), which is designed for updating from arbitrary
priors given information in the form of arbitrary constraints, includes as
special cases both MaxEnt (which allows arbitrary constraints) and Bayes' rule
(which allows arbitrary priors). Thus, ME unifies the two themes of these
workshops -- the Maximum Entropy and the Bayesian methods -- into a single
general inference scheme that allows us to handle problems that lie beyond the
reach of either of the two methods separately. I conclude with a couple of
simple illustrative examples.
