  Implicit probabilistic models are models defined naturally in terms of a
sampling procedure and often induces a likelihood function that cannot be
expressed explicitly. We develop a simple method for estimating parameters in
implicit models that does not require knowledge of the form of the likelihood
function or any derived quantities, but can be shown to be equivalent to
maximizing likelihood under some conditions. Our result holds in the
non-asymptotic parametric setting, where both the capacity of the model and the
number of data examples are finite. We also demonstrate encouraging
experimental results.
