Likelilood ratio gradient estimation
- 1 January 1987
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 366-375
- https://doi.org/10.1145/318371.318612
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.Keywords
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