Recurrent machines for likelihood-free inference

By Arthur Pesah, Antoine Wehenkel, Gilles Louppe. 2018

5 citations recorded by the source corpus.

Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a likelihood function, most of the existing inference methods optimize the simulator parameters through a handcrafted iterative procedure that tries to make the simulated data more similar to the observations. In this work, we explore whether meta-learning can be used in the likelihood-free context, for learning automatically from data an iterative optimization procedure that would solve likelihood-free inference problems. We design a recurrent inference machine that learns a sequence of parameter updates leading to good parameter estimates, without ever specifying some explicit notion of divergence between the simulated data and the real data distributions. We demonstrate our approach on toy simulators, showing promising results both in terms of performance and robustness.

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Automatic Posterior Transformation for Likelihood-Free Inference (2019, background): “Some LFI meth-ods instead aim to infer a point or local estimate of the parameters (Pesah et al., 2018; Louppe & Cranmer, 2017; McCarthy et al., 2017).”
Likelihood-free MCMC with Amortized Approximate Ratio Estimators (2019, background): “Others have investigated meta-learning to learn parameter updates (Pesah et al., 2018).”