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GATSBI Generative Adversarial Training for Simulation-Based Inference

P Ramesh, JM Lueckmann, J Boelts… - arXiv preprint arXiv …, 2022 - arxiv.org
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… Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative …

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@article{2203.06481v1,
Author        = {Poornima Ramesh and Jan-Matthis Lueckmann and Jan Boelts and Álvaro Tejero-Cantero and David S. Greenberg and Pedro J. Gonçalves and Jakob H. Macke},
Title         = {GATSBI: Generative Adversarial Training for Simulation-Based Inference},
Eprint        = {2203.06481v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Simulation-based inference (SBI) refers to statistical inference on
stochastic models for which we can generate samples, but not compute
likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not
require explicit likelihoods. We study the relationship between SBI and GANs,
and introduce GATSBI, an adversarial approach to SBI. GATSBI reformulates the
variational objective in an adversarial setting to learn implicit posterior
distributions. Inference with GATSBI is amortised across observations, works in
high-dimensional posterior spaces and supports implicit priors. We evaluate
GATSBI on two SBI benchmark problems and on two high-dimensional simulators. On
a model for wave propagation on the surface of a shallow water body, we show
that GATSBI can return well-calibrated posterior estimates even in high
dimensions. On a model of camera optics, it infers a high-dimensional posterior
given an implicit prior, and performs better than a state-of-the-art SBI
approach. We also show how GATSBI can be extended to perform sequential
posterior estimation to focus on individual observations. Overall, GATSBI opens
up opportunities for leveraging advances in GANs to perform Bayesian inference
on high-dimensional simulation-based models.},
Year          = {2022},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2203.06481v1},
File          = {2203.06481v1.pdf}
}

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