BibTeX

@article{2205.15784v1,
Author        = {Lorenzo Pacchiardi and Ritabrata Dutta},
Title         = {Likelihood-Free Inference with Generative Neural Networks via Scoring
  Rule Minimization},
Eprint        = {2205.15784v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.CO},
Abstract      = {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]
approximated instead the posterior with generative networks, which drop the
invertibility requirement and are thus a more flexible class of distributions
scaling to high-dimensional and structured data. However, generative networks
only allow sampling from the parametrized distribution; for this reason, Ramesh
et al. [2022] follows the common solution of adversarial training, where the
generative network plays a min-max game against a "critic" network. This
procedure is unstable and can lead to a learned distribution underestimating
the uncertainty - in extreme cases collapsing to a single point. Here, we
propose to approximate the posterior with generative networks trained by
Scoring Rule minimization, an overlooked adversarial-free method enabling
smooth training and better uncertainty quantification. In simulation studies,
the Scoring Rule approach yields better performances with shorter training time
with respect to the adversarial framework.},
Year          = {2022},
Month         = {May},
Url           = {http://arxiv.org/abs/2205.15784v1},
File          = {2205.15784v1.pdf}
}