Simulation-based inference (SBI) techniques are now an essential tool for the parameter estimation of mechanistic and simulatable models with intractable likelihoods. Statistical approaches to SBI such as approximate Bayesian computation and Bayesian synthetic likelihood have been well studied in the well specified and misspecified settings. However, most implementations are inefficient in that many model simulations are wasted. Neural approaches such as sequential neural likelihood (SNL) have been developed that exploit all model simulations to build a surrogate of the likelihood function. However, SNL approaches have been shown to perform poorly under model misspecification. In this paper, we develop a new method for SNL that is robust to model misspecification and can identify areas where the model is deficient. We demonstrate the usefulness of the new approach on several illustrative examples.

翻译：基于模拟的推断（Simulation-based inference, SBI）技术如今已成为对具有不可处理似然函数的机制性与可模拟模型进行参数估计的重要工具。在模型设定正确与误设定情形下，近似贝叶斯计算与贝叶斯合成似然等SBI统计方法已得到充分研究。然而，大多数实现方法效率低下，导致大量模型模拟被浪费。序贯神经似然（Sequential Neural Likelihood, SNL）等神经方法已被开发出来，能够利用所有模型模拟构建似然函数的替代模型。然而，已有研究表明SNL方法在模型误设定下表现不佳。本文提出一种对模型误设定具有鲁棒性且能识别模型缺陷区域的新型SNL方法。我们通过多个示例验证了该新方法的有效性。