Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization

@article{Pacchiardi2022LikelihoodFreeIW,
  title={Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization},
  author={Lorenzo Pacchiardi and Ritabrata Dutta},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.15784}
}
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… 

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