BibTeX

@article{2504.09475v1,
Author        = {Wang Yuyan and Michael Evans and David J. Nott},
Title         = {Robust Bayesian methods using amortized simulation-based inference},
Eprint        = {2504.09475v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Bayesian simulation-based inference (SBI) methods are used in statistical
models where simulation is feasible but the likelihood is intractable. Standard
SBI methods can perform poorly in cases of model misspecification, and there
has been much recent work on modified SBI approaches which are robust to
misspecified likelihoods. However, less attention has been given to the issue
of inappropriate prior specification, which is the focus of this work. In
conventional Bayesian modelling, there will often be a wide range of prior
distributions consistent with limited prior knowledge expressed by an expert.
Choosing a single prior can lead to an inappropriate choice, possibly
conflicting with the likelihood information. Robust Bayesian methods, where a
class of priors is considered instead of a single prior, can address this
issue. For each density in the prior class, a posterior can be computed, and
the range of the resulting inferences is informative about posterior
sensitivity to the prior imprecision. We consider density ratio classes for the
prior and implement robust Bayesian SBI using amortized neural methods
developed recently in the literature. We also discuss methods for checking for
conflict between a density ratio class of priors and the likelihood, and
sequential updating methods for examining conflict between different groups of
summary statistics. The methods are illustrated for several simulated and real
examples.},
Year          = {2025},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2504.09475v1},
File          = {2504.09475v1.pdf}
}