  In this paper we consider the problem of Learning from Satisfying Assignments
introduced by \cite{1} of finding a distribution that is a close approximation
to the uniform distribution over the satisfying assignments of a low complexity
Boolean function $f$. In a later work \cite{2} consider the same problem but
with the knowledge of some continuous distribution $D$ and the objective being
to estimate $D_f$, which is $D$ restricted to the satisfying assignments of an
unknown Boolean function $f$. We consider these problems from the point of view
of parameter estimation techniques in statistical machine learning and prove
similar results that are based on standard optimization algorithms for Risk
Minimization.
