This paper presents recent methodological advances to perform simulation-based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for model misspecification. Our approach is based on a two-step framework. First, the latent function that appears as second layer of the BHM is inferred and used to diagnose possible model misspecification. Second, target parameters of the trusted model are inferred via SBI. Simulations used in the first step are recycled for score compression, which is necessary to the second step. As a proof of concept, we apply our framework to a prey-predator model built upon the Lotka-Volterra equations and involving complex observational processes.

翻译：本文件介绍了最近在对贝叶斯人等级模型的一般类别进行模拟推论(SBI)方面的最新方法进展,同时检查模型的分类错误。我们的方法基于一个两步框架。首先,作为BHM第二层的潜伏功能被推断出来,用来诊断可能的模型的分类错误。第二,通过履行机构推断出可信赖模型的目标参数。第一步使用的模拟为计分压缩进行再循环,这是第二步所必需的。作为概念的证明,我们将我们的框架应用于基于Lotka-Volterra方程式的猎物掠夺者模型,并涉及复杂的观察过程。