Likelihood-free stochastic approximation EM for inference in complex models |
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Authors: | Umberto Picchini |
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Institution: | Centre for Mathematical Sciences, Lund University, S?lvegatan 18, Lund, Sweden |
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Abstract: | 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. |
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Keywords: | Incomplete data Intractable likelihood SAEM State-space model Synthetic likelihood |
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