Likelihood Ratio Gradient Estimation: An Overview
P. W. Glynn
Proceedings of the 1987 Winter Simulation Conference, 366-375 (1987)
The likelihood ratio method for gradient estimation is briefly surveyed. Two applications settings are described, namely Monte Carlo optimization and statistical analysis of complex stochastic systems. Steady-state gradient estimation is emphasized, and both regenerative and non-regenerative approaches are given. The paper also indicates how these methods apply to general discrete-event simulations; the idea is to view such systems as general state space Markov chains.