  The convergence properties of the stationary Fokker-Planck algorithm for the
estimation of the asymptotic density of stochastic search processes is studied.
Theoretical and empirical arguments for the characterization of convergence of
the estimation in the case of separable and nonseparable nonlinear optimization
problems are given. Some implications of the convergence of stationary
Fokker-Planck learning for the inference of parameters in artificial neural
network models are outlined.
