A general framework for cutting feedback within modularized Bayesian inference
本文正式定义了模块化贝叶斯推断中的“模块”，并提出了在任意有向无环图结构中识别模块、确定顺序及构建切断分布的方法，将两模块切断推断推广到多模块情形。
Abstract Standard Bayesian inference enables building models that combine information from various sources, but this inference may not be reliable if components of the model are misspecified. Cut inference, a particular type of modularized Bayesian inference, is an alternative that splits a model into modules and cuts the feedback from any suspect module. Previous studies have focused on a two module case, but a more general definition of a ‘module’ remains unclear. We present a formal definition of a ‘module’ and discuss its properties. We formulate methods for identifying modules; determining the order of modules; and building the cut distribution that should be used for cut inference within an arbitrary directed acyclic graph structure. We justify the cut distribution by showing that it not only cuts the feedback but also is the best approximation to the joint distribution satisfying this condition in Kullback–Leibler divergence. We also extend cut inference for the two module case to a general multiple-module case via a sequential splitting technique and demonstrate this via illustrative applications.