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 an algorithm to identify sparse dependence structure in continuousand non-Gaussian probability distributions, given a corresponding set of data.The conditional independence structure of an arbitrary distribution can berepresented as an undirected graph (or Markov random field), but mostalgorithms for learning this structure are restricted to the discrete orGaussian cases. Our new approach allows for more realistic and accuratedescriptions of the distribution in question, and in turn better estimates ofits sparse Markov structure. Sparsity in the graph is of interest as it canaccelerate inference, improve sampling methods, and reveal importantdependencies between variables. The algorithm relies on exploiting theconnection between the sparsity of the graph and the sparsity of transportmaps, which deterministically couple one probability measure to another.

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting