The DOI matches its Crossref record.
Likelilood ratio gradient estimation: an overview
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Grondman, I., Busoniu, L., Lopes, G. A. D., & Babuska, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218595 · Cited by 1,042
Reinforcement learning of motor skills with policy gradients
Peters, J., & Schaal, S. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.02.003 · Cited by 831
Monte Carlo methods for security pricing
Boyle, P., Broadie, M., & Glasserman, P. (1997) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(97)00028-6 · Cited by 854
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2016) · arXiv · doi:10.48550/arxiv.1610.03295 · Cited by 509
Metadata
- Authors
- Glynn, P. W.
- Venue
- Proceedings of the 19th conference on Winter simulation - WSC '87
- Publisher
- ACM Press
- Published
- 1987
- Pages
- 366-375
- ISSN
- 0891-7736 (ISSN-L)
- OpenAlex
- W2006036383
- Cited by
- 219
- References
- 0
- FWCI
- 9.47
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Likelilood ratio gradient estimation: an overview. https://app.cochat.ai/papers/10.1145/318371.318612
- The paper
- Glynn, P. W. (1987). Likelilood ratio gradient estimation: an overview. Proceedings of the 19th conference on Winter simulation - WSC '87. https://doi.org/10.1145/318371.318612
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
