Stochastic approximation for Monte Carlo optimization (1986)
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Stochastic approximation for Monte Carlo optimization
WSC '86: Proceedings of the 18th conference on Winter simulationIn this paper, we introduce two convergent Monte Carlo algorithms for optimizing complex stochastic systems. The first algorithm, which is applicable to regenerative processes, operates by estimating finite differences. The second method is of Robbins-...
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo: Application to maximum marginal likelihood and empirical Bayesian estimation
AbstractStochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in problems with missing or incomplete data, and in parametric ...
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- IIE: Institute of Industrial Engineers
- INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
- ASA: American Statistical Association
- IEEE/SMC: Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- NIST: National Institute of Standards and Technology
- (SCS): The Society for Modeling and Simulation International
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