BibTeX

@article{2310.17009v2,
Author        = {Yuling Yao and Bruno Régaldo-Saint Blancard and Justin Domke},
Title         = {Simulation-based stacking},
Eprint        = {2310.17009v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Simulation-based inference has been popular for amortized Bayesian
computation. It is typical to have more than one posterior approximation, from
different inference algorithms, different architectures, or simply the
randomness of initialization and stochastic gradients. With a consistency
guarantee, we present a general posterior stacking framework to make use of all
available approximations. Our stacking method is able to combine densities,
simulation draws, confidence intervals, and moments, and address the overall
precision, calibration, coverage, and bias of the posterior approximation at
the same time. We illustrate our method on several benchmark simulations and a
challenging cosmological inference task.},
Year          = {2023},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2310.17009v2},
File          = {2310.17009v2.pdf}
}