Likelilood ratio gradient estimation: an overview

摘要: 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 emphasized, both regenerative non-regenerative approaches given. paper also indicates how these methods apply to general discrete-event simulations; the idea view such systems as state space Markov chains.