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

In this article, we derive a novel non-reversible, continuous-time Markovchain Monte Carlo (MCMC) sampler, called Coordinate Sampler, based on apiecewise deterministic Markov process (PDMP), which can be seen as a variantof the Zigzag sampler. In addition to proving a theoretical validation for thisnew sampling algorithm, we show that the Markov chain it induces exhibitsgeometrical ergodicity convergence, for distributions whose tails decay atleast as fast as an exponential distribution and at most as fast as a Gaussiandistribution. Several numerical examples highlight that our coordinate sampleris more efficient than the Zigzag sampler, in terms of effective sample size.

The Coordinate Sampler: A Non-Reversible Gibbs-like MCMC Sampler