ALT A general framework for cutting feedback within modularized Bayesian inference by Yang Liu and Robert J. B. Goudie 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...