On Contrastive Learning for Likelihood-free Inference

Code

• github.com/conormdurkan/lfi
  OfficialIn paperpytorch★ 39
• github.com/stephengreen/lfi-gw
  pytorch★ 42
• github.com/AI-HPC-Research-Team/GW
  pytorch★ 10
• github.com/bkmi/cnre
  pytorch★ 2
• github.com/mackelab/nflows-nofork
  pytorch★ 0

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.

Tasks

Reproductions