Workbronze1987

Likelilood ratio gradient estimation

219 citations0/0 files storedWinter Simulation Conference

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Simulation Techniques and Applications Stochastic processes and financial applications Markov Chains and Monte Carlo Methods

Abstract

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.

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1990A Unified View of the IPA, SF, and LR Gradient Estimation Techniques 1996Nelder-Mead Simplex Modifications for Simulation Optimization 1999Simulation optimization methodologies 1992Metamodels for simulation input-output relations 1996Sample-path optimization of convex stochastic performance functions 1989Techniques for simulation response optimization 1992A tutorial on simulation optimization 2005Sensitivity Analysis Using Itô--Malliavin Calculus and Martingales, and Application to Stochastic Optimal Control

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