  Consider the observation of n iid realizations of an experiment with d>1
possible outcomes, which corresponds to a single observation of a multinomial
distribution M(n,p) where p is an unknown discrete distribution on {1,...,d}.
In many applications, the construction of a confidence region for p when n is
small is crucial. This concrete challenging problem has a long history. It is
well known that the confidence regions built from asymptotic statistics do not
have good coverage when n is small. On the other hand, most available methods
providing non-asymptotic regions with controlled coverage are limited to the
binomial case d=2. In the present work, we propose a new method valid for any
d>1. This method provides confidence regions with controlled coverage and small
volume, and consists of the inversion of the "covering collection"' associated
with level-sets of the likelihood. The behavior when d/n tends to infinity
remains an interesting open problem beyond the scope of this work.
