Robust Approximate Bayesian Computation: An Adjustment Approach

2020/08/07 by David T. Frazier, Christopher Drovandi, Frazier, David T. +4 · 1 citation
Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.CO #stat.ME

paper · pdf · doi:10.48550/arxiv.2008.04099

arXiv admin note: text overlap with arXiv:1904.04551

arxiv created 2020/08/07 · openalex publication_date 2020/08/07 · arxiv updated 2020/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally applied to rejection-based ABC and to popular regression adjustment ABC. We demonstrate that this new approach mitigates the poor performance of regression adjusted ABC that can eventuate when the model is misspecified. In addition, this new adjustment approach allows us to detect which features of the observed data can not be reliably reproduced by the assumed model. A series of simulated and empirical examples illustrate this new approach.

Citations

• Statistical inference for noisy nonlinear ecological dynamic systems
• Robust Approximate Bayesian Inference with Synthetic Likelihood

Cited by

• Synthetic likelihood in misspecified models

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