Entropic Priors and Bayesian Model Selection
  We demonstrate that the principle of maximum relative entropy (ME), used
judiciously, can ease the specification of priors in model selection problems.
The resulting effect is that models that make sharp predictions are
disfavoured, weakening the usual Bayesian "Occam's Razor". This is illustrated
with a simple example involving what Jaynes called a "sure thing" hypothesis.
Jaynes' resolution of the situation involved introducing a large number of
alternative "sure thing" hypotheses that were possible before we observed the
data. However, in more complex situations, it may not be possible to explicitly
enumerate large numbers of alternatives. The entropic priors formalism produces
the desired result without modifying the hypothesis space or requiring explicit
enumeration of alternatives; all that is required is a good model for the prior
predictive distribution for the data. This idea is illustrated with a simple
rigged-lottery example, and we outline how this idea may help to resolve a
recent debate amongst cosmologists: is dark energy a cosmological constant, or
has it evolved with time in some way? And how shall we decide, when the data
are in?
