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 present paper focuses on the problem of sampling from a given targetdistribution $\pi$ defined on some general state space. To this end, weintroduce a novel class of non-reversible Markov chains, each chain beingdefined on an extended state space and having an invariant probability measureadmitting $\pi$ as a marginal distribution. The proposed methodology isinspired by a new formulation of Kac's theorem and allows global and localdynamics to be smoothly combined. Under mild conditions, the correspondingMarkov transition kernel can be shown to be irreducible and Harris recurrent.In addition, we establish that geometric ergodicity holds under appropriateconditions on the global and local dynamics. Finally, we illustrate numericallythe use of the proposed method and its potential benefits in comparison toexisting Markov chain Monte Carlo (MCMC) algorithms.

Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm