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

We introduce two novel non-parametric statistical hypothesis tests. The firsttest, called the relative test of dependency, enables us to determine whetherone source variable is significantly more dependent on a first target variableor a second. Dependence is measured via the Hilbert-Schmidt IndependenceCriterion (HSIC). The second test, called the relative test of similarity, isuse to determine which of the two samples from arbitrary distributions issignificantly closer to a reference sample of interest and the relative measureof similarity is based on the Maximum Mean Discrepancy (MMD). To constructthese tests, we have used as our test statistics the difference of HSICstatistics and of MMD statistics, respectively. The resulting tests areconsistent and unbiased, and have favorable convergence properties. Theeffectiveness of the relative dependency test is demonstrated on severalreal-world problems: we identify languages groups from a multilingual parallelcorpus, and we show that tumor location is more dependent on gene expressionthan chromosome imbalance. We also demonstrate the performance of the relativetest of similarity over a broad selection of model comparisons problems in deepgenerative models.

Fast Non-Parametric Tests of Relative Dependency and Similarity