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.

翻译：基于模拟的推断技术对于估计具有难解似然的机制性和可模拟模型参数至关重要。虽然传统统计方法（如近似贝叶斯计算和贝叶斯合成似然）已在良好设定与误设定条件下得到研究，但它们常因浪费模型模拟而效率低下。神经方法（如顺序神经似然）通过利用所有模型模拟训练似然函数的神经替代模型避免了这一浪费。然而，顺序神经似然在模型误设定下的表现不可靠，可能导致过度自信的后验集中于不准确的参数估计。本文提出一种新颖的顺序神经似然方法，通过引入额外调整参数，该方法对模型误设定具有鲁棒性，且能够识别模型无法恢复的数据特征。通过多个示例验证，我们的方法相比顺序神经似然能给出更准确的点估计与不确定性量化。