BibTeX

@article{2301.11873v4,
Author        = {Lasse Elsemüller and Martin Schnuerch and Paul-Christian Bürkner and Stefan T. Radev},
Title         = {A Deep Learning Method for Comparing Bayesian Hierarchical Models},
Eprint        = {2301.11873v4},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Bayesian model comparison (BMC) offers a principled approach for assessing
the relative merits of competing computational models and propagating
uncertainty into model selection decisions. However, BMC is often intractable
for the popular class of hierarchical models due to their high-dimensional
nested parameter structure. To address this intractability, we propose a deep
learning method for performing BMC on any set of hierarchical models which can
be instantiated as probabilistic programs. Since our method enables amortized
inference, it allows efficient re-estimation of posterior model probabilities
and fast performance validation prior to any real-data application. In a series
of extensive validation studies, we benchmark the performance of our method
against the state-of-the-art bridge sampling method and demonstrate excellent
amortized inference across all BMC settings. We then showcase our method by
comparing four hierarchical evidence accumulation models that have previously
been deemed intractable for BMC due to partly implicit likelihoods.
Additionally, we demonstrate how transfer learning can be leveraged to enhance
training efficiency. We provide reproducible code for all analyses and an
open-source implementation of our method.},
Year          = {2023},
Month         = {Jan},
Url           = {http://arxiv.org/abs/2301.11873v4},
File          = {2301.11873v4.pdf}
}