  Optimum parameter estimation methods require knowledge of a parametric
probability density that statistically describes the available observations. In
this work we examine Bayesian and non-Bayesian parameter estimation problems
under a data-driven formulation where the necessary parametric probability
density is replaced by available data. We present various data-driven versions
that either result in neural network approximations of the optimum estimators
or in well defined optimization problems that can be solved numerically. In
particular, for the data-driven equivalent of non-Bayesian estimation we end up
with optimization problems similar to the ones encountered for the design of
generative networks.
