BibTeX

@article{2302.09125v3,
Author        = {Stefan T. Radev and Marvin Schmitt and Valentin Pratz and Umberto Picchini and Ullrich Köthe and Paul-Christian Bürkner},
Title         = {JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models},
Eprint        = {2302.09125v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {This work proposes ``jointly amortized neural approximation'' (JANA) of
intractable likelihood functions and posterior densities arising in Bayesian
surrogate modeling and simulation-based inference. We train three complementary
networks in an end-to-end fashion: 1) a summary network to compress individual
data points, sets, or time series into informative embedding vectors; 2) a
posterior network to learn an amortized approximate posterior; and 3) a
likelihood network to learn an amortized approximate likelihood. Their
interaction opens a new route to amortized marginal likelihood and posterior
predictive estimation -- two important ingredients of Bayesian workflows that
are often too expensive for standard methods. We benchmark the fidelity of JANA
on a variety of simulation models against state-of-the-art Bayesian methods and
propose a powerful and interpretable diagnostic for joint calibration. In
addition, we investigate the ability of recurrent likelihood networks to
emulate complex time series models without resorting to hand-crafted summary
statistics.},
Year          = {2023},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2302.09125v3},
File          = {2302.09125v3.pdf}
}