BibTeX

@article{2209.01845v1,
Author        = {Patrick Cannon and Daniel Ward and Sebastian M. Schmon},
Title         = {Investigating the Impact of Model Misspecification in Neural
  Simulation-based Inference},
Eprint        = {2209.01845v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Aided by advances in neural density estimation, considerable progress has
been made in recent years towards a suite of simulation-based inference (SBI)
methods capable of performing flexible, black-box, approximate Bayesian
inference for stochastic simulation models. While it has been demonstrated that
neural SBI methods can provide accurate posterior approximations, the
simulation studies establishing these results have considered only
well-specified problems -- that is, where the model and the data generating
process coincide exactly. However, the behaviour of such algorithms in the case
of model misspecification has received little attention. In this work, we
provide the first comprehensive study of the behaviour of neural SBI algorithms
in the presence of various forms of model misspecification. We find that
misspecification can have a profoundly deleterious effect on performance. Some
mitigation strategies are explored, but no approach tested prevents failure in
all cases. We conclude that new approaches are required to address model
misspecification if neural SBI algorithms are to be relied upon to derive
accurate scientific conclusions.},
Year          = {2022},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2209.01845v1},
File          = {2209.01845v1.pdf}
}