Title: Maximum Entropy competes with Maximum Likelihood. Abstract: Maximum entropy (MAXENT) method has a large number of
applications in theoretical and applied machine learning, since it
provides a convenient non-parametric tool for estimating unknown
probabilities.  The method is a major contribution of statistical
physics to probabilistic inference. However, a systematic approach
towards its validity limits is currently missing.  Here we study MAXENT
in a Bayesian decision theory set-up, i.e. assuming that there exists a
well-defined prior Dirichlet density for unknown probabilities, and that
the average Kullback-Leibler (KL) distance can be employed for deciding
on the quality and applicability of various estimators.  These allow to
evaluate the relevance of various MAXENT constraints, check its general
applicability, and compare MAXENT with estimators having various degrees
of dependence on the prior, {\it viz.} the regularized maximum
likelihood (ML) and the Bayesian estimators. We show that MAXENT applies
in sparse data regimes, but needs specific types of prior information. 
In particular, MAXENT can outperform the optimally regularized
ML provided that there are prior rank correlations between the estimated
random quantity and its probabilities. 