mlsanityData to 7 Oct 2026

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference

arXiv 2301.13368 ↗First posted 31 Jan 2023stat.ME cs.LG stat.COcited by 8

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Abstract (arXiv)

Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical approaches like approximate Bayesian computation and Bayesian synthetic likelihood have been studied under well-specified and misspecified settings, they often suffer from inefficiencies due to wasted model simulations. Neural approaches, such as sequential neural likelihood (SNL) avoid this wastage by utilising all model simulations to train a neural surrogate for the likelihood function. However, the performance of SNL under model misspecification is unreliable and can result in overconfident posteriors centred around an inaccurate parameter estimate. In this paper, we propose a novel SNL method, which through the incorporation of additional adjustment parameters, is robust to model misspecification and capable of identifying features of the data that the model is not able to recover. We demonstrate the efficacy of our approach through several illustrative examples, where our method gives more accurate point estimates and uncertainty quantification than SNL.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 55 of its 112 references that are in the map. First posted on arXiv 31 Jan 2023.

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55 of 112 references.

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Cited by year

Citing papers per year: 2023: 3; 2024: 4; 2025: 1.

8 papers.

  1. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  2. 2024Neural Methods for Amortized Inferencecited by 36
  3. 2024Addressing Misspecification in Simulation-based Inference through Data-driven Calibrationcited by 26
  4. 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationcited by 17
  5. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  6. 2024Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigationcited by 8
  7. 2024Preconditioned Neural Posterior Estimation for Likelihood-free Inferencecited by 6
  8. 2023Bayesian calibration of differentiable agent-based modelscited by 4