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/

Zendy Plus

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

Zendy Tools

Zendy Open

Due to the escalating growth of big data sets in recent years, new BayesianMarkov chain Monte Carlo (MCMC) parallel computing methods have been developed.These methods partition large data sets by observations into subsets. However,for Bayesian nested hierarchical models, typically only a few parameters arecommon for the full data set, with most parameters being group-specific. Thus,parallel Bayesian MCMC methods that take into account the structure of themodel and split the full data set by groups rather than by observations are amore natural approach for analysis. Here, we adapt and extend a recentlyintroduced two-stage Bayesian hierarchical modeling approach, and we partitioncomplete data sets by groups. In stage 1, the group-specific parameters areestimated independently in parallel. The stage 1 posteriors are used asproposal distributions in stage 2, where the target distribution is the fullmodel. Using three-level and four-level models, we show in both simulation andreal data studies that results of our method agree closely with the full dataanalysis, with greatly increased MCMC efficiency and greatly reducedcomputation times. The advantages of our method versus existing parallel MCMCcomputing methods are also described.

Parallel Markov Chain Monte Carlo for Bayesian Hierarchical Models with Big Data, in Two Stages