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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.
|Number of pages||26|
|Journal||Communications in Statistics: Simulation and Computation|
|Early online date||2018 Jan 18|
|Publication status||Published - 2019|
Subject classification (UKÄ)
- Probability Theory and Statistics
- maximum likelihood
- sequential Monte Carlo
- synthetic likelihood;
- state space model
- Stochastic differential equation
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Stochastic modelling of protein folding and likelihood-free statistical inference methods
Picchini, U., Forman, J., Lindorff-Larsen, K. & Wiqvist, S.
2015/01/01 → …