A general framework for cutting feedback within modularized Bayesian inference
模块化贝叶斯推断中切断反馈的一般框架
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…