  These lectures deal with the problem of inductive inference, that is, the
problem of reasoning under conditions of incomplete information. Is there a
general method for handling uncertainty? Or, at least, are there rules that
could in principle be followed by an ideally rational mind when discussing
scientific matters? What makes one statement more plausible than another? How
much more plausible? And then, when new information is acquired how do we
change our minds? Or, to put it differently, are there rules for learning? Are
there rules for processing information that are objective and consistent? Are
they unique? And, come to think of it, what, after all, is information? It is
clear that data contains or conveys information, but what does this precisely
mean? Can information be conveyed in other ways? Is information physical? Can
we measure amounts of information? Do we need to? Our goal is to develop the
main tools for inductive inference--probability and entropy--from a thoroughly
Bayesian point of view and to illustrate their use in physics with examples
borrowed from the foundations of classical statistical physics.
