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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In experiments to estimate parameters of a parametric model, Bayesianexperiment design allows measurement settings to be chosen based on utility,which is the predicted improvement of parameter distributions due to modeledmeasurement results. In this paper we compare information-theory-based utilitywith three alternative utility algorithms. Tests of these utility alternativesin simulated adaptive measurements demonstrate large improvements incomputational speed with slight impacts on measurement efficiency.

Simplified algorithms for adaptive experiment design in parameter estimation