English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Bayesian inference has widely acknowledged advantages in many problems, butit can also be unreliable if the model is misspecified. Bayesian modularinference is concerned with inference in complex models which have beenspecified through a collection of coupled sub-models. The sub-models are calledmodules in the literature, and they often arise from modeling different datasources, or from combining domain knowledge from different disciplines. Whensome modules are misspecified, cutting feedback is a widely used Bayesianmodular inference method which ensures that information from suspect modelcomponents is not used in making inferences about parameters in correctlyspecified modules. However, in general settings it is difficult to decide whenthis ``cut posterior'' is preferable to the exact posterior. Whenmisspecification is not severe, cutting feedback may increase the uncertaintyin Bayesian posterior inference greatly without reducing estimation biassubstantially. This motivates semi-modular inference methods, which avoid thebinary cut of cutting feedback approaches. In this work, using a local modelmisspecification framework, we provide the first precise formulation of the thebias-variance trade-off that has motivated the literature on semi-modularinference. We then implement a mixture-based semi-modular inference approach,demonstrating theoretically that it delivers inferences that are more accurate,in terms of a user-defined loss function, than if either the cut or fullposterior were used by themselves. The new method is demonstrated in a numberof applications.

Accurate semi-modular posterior inference with a user-defined loss function