Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components

arXiv2003.06804MaRDI QIDQ6336776

Author name not available (Why is that?)

Publication date: 15 March 2020

Abstract: Bayesian statistical inference loses predictive optimality when generative models are misspecified. Working within an existing coherent loss-based generalisation of Bayesian inference, we show existing Modular/Cut-model inference is coherent, and write down a new family of Semi-Modular Inference (SMI) schemes, indexed by an influence parameter, with Bayesian inference and Cut-models as special cases. We give a meta-learning criterion and estimation procedure to choose the inference scheme. This returns Bayesian inference when there is no misspecification. The framework applies naturally to Multi-modular models. Cut-model inference allows directed information flow from well-specified modules to misspecified modules, but not vice versa. An existing alternative power posterior method gives tunable but undirected control of information flow, improving prediction in some settings. In contrast, SMI allows tunable and directed information flow between modules. We illustrate our methods on two standard test cases from the literature and a motivating archaeological data set.

Has companion code repository: https://github.com/chriscarmona/aistats2020smi

This page was built for publication: Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6336776)