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

Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are findingwidespread use in applied statistics and machine learning. These often lead todifficult computational problems, which are increasingly being solved onparallel and distributed systems such as compute clusters. Recent work hasproposed running iterative algorithms such as gradient descent and MCMC inparallel asynchronously for increased performance, with good empirical resultsin certain problems. Unfortunately, for MCMC this parallelization techniquerequires new convergence theory, as it has been explicitly demonstrated to leadto divergence on some examples. Recent theory on Asynchronous Gibbs samplingdescribes why these algorithms can fail, and provides a way to alter them tomake them converge. In this article, we describe how to apply this theory in ageneric setting, to understand the asynchronous behavior of any MCMC algorithm,including those implemented using parameter servers, and those not based onGibbs sampling.

Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods