论文标题
在检查模型错误时，基于仿真的贝叶斯分层模型推断
Simulation-based inference of Bayesian hierarchical models while checking for model misspecification
论文作者
论文摘要
本文介绍了在检查模型错误指定的同时，对一般类贝叶斯分层模型（BHM）进行基于模拟的推理（SBI）的方法学进展。我们的方法基于两个步骤的框架。首先，推断出作为BHM第二层的潜在函数是被推断出来的，并用于诊断可能的模型错误指定。其次，通过SBI推断受信任模型的目标参数。第一步中使用的仿真被回收以进行分数压缩，这是第二步所必需的。作为概念的证明，我们将框架应用于基于Lotka-volterra方程的猎物预言模型，并涉及复杂的观察过程。
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.
