Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Likelihood-free stochastic approximation EM for inference in complex
  models   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.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas