Learning Robust Statistics for Simulation-based Inference under Model Misspecification

Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi, Samuel Kaski

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
Titel på värdpublikationNeurIPS 2023
FörlagMorgan Kaufmann Publishers
Utgivningsdatumdec. 2023
DOI
StatusPublicerad - dec. 2023
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangConference on Neural Information Processing Systems 2023 - New Orleans, Förenta Staterna (USA)
Varaktighet: 10 dec. 202316 dec. 2023
https://neurips.cc/

Publikationsserier

NamnAdvances in Neural Information Processing Systems
Volym36
ISSN (elektroniskt)1049-5258

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap
  • 112 Statistik

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