Stochastic approximation cut algorithm for inference in modularized Bayesian models.

ABSTRACT: Bayesian modelling enables us to accommodate complex forms of data and make a comprehensive inference, but the effect of partial misspecification of the model is a concern. One approach in this setting is to modularize the model and prevent feedback from suspect modules, using a cut model. After observing data, this leads to the cut distribution which normally does not have a closed form. Previous studies have proposed algorithms to sample from this distribution, but these algorithms have unclear theoretical convergence properties. To address this, we propose a new algorithm called the stochastic approximation cut (SACut) algorithm as an alternative. The algorithm is divided into two parallel chains. The main chain targets an approximation to the cut distribution; the auxiliary chain is us

SUBMITTER: Liu Y 

PROVIDER: S-EPMC7612314 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature