Likelihood-free stochastic approximation EM for inference in complex models

Research output: Contribution to journalArticlepeer-review


A maximum likelihood methodology for the parameters of models with an intractable likelihood is introduced. We produce a likelihood-free version of the stochastic approximation expectation-maximization (SAEM) algorithm to maximize the likelihood function of model parameters. While SAEM is best suited for models having a tractable "complete likelihood" function, its application to moderately complex models is a difficult or even impossible task. We show how to construct a likelihood-free version of SAEM by using the "synthetic likelihood" paradigm. Our method is completely plug-and-play, requires almost no tuning and can be applied to both static and dynamic models. Four simulation studies illustrate the method, including a stochastic differential equation model, a stochastic Lotka-Volterra model and data from g-and-k distributions. MATLAB code is available as supplementary material.
Original languageEnglish
Pages (from-to)861-881
Number of pages26
JournalCommunications in Statistics: Simulation and Computation
Issue number3
Early online date2018 Jan 18
Publication statusPublished - 2019

Subject classification (UKÄ)

  • Probability Theory and Statistics


  • maximum likelihood
  • SAEM
  • sequential Monte Carlo
  • synthetic likelihood;
  • state space model
  • Stochastic differential equation


Dive into the research topics of 'Likelihood-free stochastic approximation EM for inference in complex models'. Together they form a unique fingerprint.

Cite this