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

Variance-based sensitivity indices have established themselves as a referenceamong practitioners of sensitivity analysis of model output. It is not unusualto consider a variance-based sensitivity analysis as informative if it producesat least the first order sensitivity indices S_j and the so-called total-effectsensitivity indices T_j for all the uncertain factors of the mathematical modelunder analysis. Computational economy is critical in sensitivity analysis. Itdepends mostly upon the number of model evaluations needed to obtain stablevalues of the estimates. While efficient estimation procedures independent fromthe number of factors under analysis are available for the first order indices,this is less the case for the total sensitivity indices. When estimating T_j,one can either use a sample-based approach, whose computational cost dependsfromon the number of factors, or approaches based on meta-modelling/emulators,e.g. based on Gaussian processes. The present work focuses on sample-basedestimation procedures for T_j and tries different avenues to achieve analgorithmic improvement over the designs proposed in the existing bestpractices. We conclude that some proposed sample-based improvements found inthe literature do not work as claimed, and that improving on the existing bestpractice is indeed fraught with difficulties. We motivate our conclusionsintroducing the concepts of explorativity and efficiency of the design.

A new sample-based algorithms to compute the total sensitivity index