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/

Zendy Plus

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

Zendy Tools

Zendy Open

For predictive evaluation based on quasi-posterior distributions, we developa new information criterion, the posterior covariance information criterion(PCIC. PCIC generalises the widely applicable information criterion WAIC so asto effectively handle predictive scenarios where likelihoods for the estimationand the evaluation of the model may be different. A typical example of suchscenarios is the weighted likelihood inference, including prediction undercovariate shift and counterfactual prediction. The proposed criterion utilisesa posterior covariance form and is computed by using only one Markov chainMonte Carlo run. Through numerical examples, we demonstrate how PCIC can applyin practice. Further, we show that PCIC is asymptotically unbiased to thequasi-Bayesian generalization error under mild conditions in weighted inferencewith both regular and singular statistical models.

Posterior Covariance Information Criterion for Weighted Inference