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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When using complex Bayesian models to combine information, the checking forconsistency of the information being combined is good statistical practice.Here a new method is developed for detecting prior-data conflicts in Bayesianmodels based on comparing the observed value of a prior to posterior divergenceto its distribution under the prior predictive distribution for the data. Thedivergence measure used in our model check is a measure of how much beliefshave changed from prior to posterior, and can be thought of as a measure of theoverall size of a relative belief function. It is shown that the proposedmethod is intuitive, has desirable properties, can be extended to hierarchicalsettings, and is related asymptotically to Jeffreys' and reference priordistributions. In the case where calculations are difficult, the use ofvariational approximations as a way of relieving the computational burden issuggested. The methods are compared in a number of examples with an alternativebut closely related approach in the literature based on the prior predictivedistribution of a minimal sufficient statistic.

Checking for prior-data conflict using prior to posterior divergences