Abstract:
Model-based Bayesian evidence combination leads to models with multiple
parameteric modules. In this setting the effects of model misspecification in
one of the modules may in some cases be ameliorated by cutting the flow of
information from the misspecified module. Semi-Modular Inference (SMI) is a
framework allowing partial cuts which modulate but do not completely cut the
flow of information between modules. We show that SMI is part of a family of
inference procedures which implement partial cuts. It has been shown that
additive losses determine an optimal, valid and order-coherent belief update.
The losses which arise in Cut models and SMI are not additive. However, like
the prequential score function, they have a kind of prequential additivity
which we define. We show that prequential additivity is sufficient to determine
the optimal valid and order-coherent belief update and that this belief update
coincides with the belief update in each of our SMI schemes.