We investigate the problem of nonparametrically calibrating an input model for stochastic simulation, with only the availability of output data. We propose a formulation of entropy maximization subject to moment matching. We then convert the maximum entropy problem into a sequence of quadratic penalty minimization problems, where each element in the sequence can be solved by efficient stochastic approximation algorithms. Some preliminary numerical results will be presented to demonstrate the efficacy of the proposed approach.