Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization. (arXiv:2205.15784v1 [stat.CO])

Bayesian Likelihood-Free Inference methods yield posterior approximations for
simulator models with intractable likelihood. Recently, many works trained
neural networks to approximate either the intractable likelihood or the
posterior directly. Most proposals use normalizing flows, namely neural
networks parametrizing invertible maps used to transform samples from an
underlying base measure; the probability density of the transformed samples is
then accessible and the normalizing flow can be trained via maximum likelihood
on simulated parameter-observation pairs. A recent work [Ramesh et al., 2022] …

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