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

The effectiveness of Bayesian Additive Regression Trees (BART) has beendemonstrated in a variety of contexts including non-parametric regression andclassification. A BART scheme for estimating the intensity of inhomogeneousPoisson processes is introduced. Poisson intensity estimation is a vital taskin various applications including medical imaging, astrophysics and networktraffic analysis. The new approach enables full posterior inference of theintensity in a non-parametric regression setting. The performance of the novelscheme is demonstrated through simulation studies on synthetic and realdatasets up to five dimensions, and the new scheme is compared with alternativeapproaches.

BART-based inference for Poisson processes