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 present and evaluate the Fast (conditional) Independence Test (FIT) -- anonparametric conditional independence test. The test is based on the idea thatwhen $P(X \mid Y, Z) = P(X \mid Y)$, $Z$ is not useful as a feature to predict$X$, as long as $Y$ is also a regressor. On the contrary, if $P(X \mid Y, Z)\neq P(X \mid Y)$, $Z$ might improve prediction results. FIT applies tothousand-dimensional random variables with a hundred thousand samples in afraction of the time required by alternative methods. We provide an extensiveevaluation that compares FIT to six extant nonparametric independence tests.The evaluation shows that FIT has low probability of making both Type I andType II errors compared to other tests, especially as the number of availablesamples grows. Our implementation of FIT is publicly available.

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes