BibTeX

@article{2305.15871v3,
Author        = {Daolang Huang and Ayush Bharti and Amauri Souza and Luigi Acerbi and Samuel Kaski},
Title         = {Learning Robust Statistics for Simulation-based Inference under Model
  Misspecification},
Eprint        = {2305.15871v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Simulation-based inference (SBI) methods such as approximate Bayesian
computation (ABC), synthetic likelihood, and neural posterior estimation (NPE)
rely on simulating statistics to infer parameters of intractable likelihood
models. However, such methods are known to yield untrustworthy and misleading
inference outcomes under model misspecification, thus hindering their
widespread applicability. In this work, we propose the first general approach
to handle model misspecification that works across different classes of SBI
methods. Leveraging the fact that the choice of statistics determines the
degree of misspecification in SBI, we introduce a regularized loss function
that penalises those statistics that increase the mismatch between the data and
the model. Taking NPE and ABC as use cases, we demonstrate the superior
performance of our method on high-dimensional time-series models that are
artificially misspecified. We also apply our method to real data from the field
of radio propagation where the model is known to be misspecified. We show
empirically that the method yields robust inference in misspecified scenarios,
whilst still being accurate when the model is well-specified.},
Year          = {2023},
Month         = {May},
Url           = {http://arxiv.org/abs/2305.15871v3},
File          = {2305.15871v3.pdf}
}