Asymptotics of cut distributions and robust modular inference using Posterior Bootstrap

Understand

Bayesian inference provides a framework to combine various model components with shared parameters, allowing joint uncertainty estimation and the use of all available data sources.

• Unfortunately, misspecification of any part of the model might propagate to all other parts and can lead to unsatisfactory results.
• Cut distributions have been proposed as a remedy, where the information is prevented from flowing along certain directions.
• We study cut distributions from an asymptotic perspective and obtain a Bernstein-von Mises theorem, as well as a Laplace approximation with quantitative bounds.