On Contrastive Learning for Likelihood-free Inference

Conor Durkan; Iain Murray; George Papamakarios

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arXiv · 2002.03712

On Contrastive Learning for Likelihood-free Inference

Conor Durkan·Iain Murray·George Papamakarios

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Abstract

Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared.

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Conor Durkan, Iain Murray, George Papamakarios. 2020-12-18. On Contrastive Learning for Likelihood-free Inference. https://arxiv.org/abs/2002.03712

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