Maximum likelihood estimation by monte carlo simulation: Toward data-driven stochastic modeling

Yijie Peng*, Michael C. Fu, Bernd Heidergott, Henry Lam

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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We propose a gradient-based simulated maximum likelihood estimation to estimate unknown parameters in a stochastic model without assuming that the likelihood function of the observations is available in closed form. A key element is to develop Monte Carlo-based estimators for the density and its derivatives for the output process, using only knowledge about the dynamics of the model. We present the theory of these estimators and demonstrate how our approach can handle various types of model structures. We also support our findings and illustrate the merits of our approach with numerical results.

Original languageEnglish
Pages (from-to)1896-1912
Number of pages17
JournalOperations Research
Issue number6
Early online date26 Oct 2020
Publication statusPublished - Dec 2020


  • Generalized likelihood ratio method
  • Gradient-based MLE
  • Sensitivity analysis
  • Simulation

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