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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recent advances in big data and analytics research have provided a wealth oflarge data sets that are too big to be analyzed in their entirety, due torestrictions on computer memory or storage size. New Bayesian methods have beendeveloped for large data sets that are only large due to large sample sizes;these methods partition big data sets into subsets, and perform independentBayesian Markov chain Monte Carlo analyses on the subsets. The methods thencombine the independent subset posterior samples to estimate a posteriordensity given the full data set. These approaches were shown to be effectivefor Bayesian models including logistic regression models, Gaussian mixturemodels and hierarchical models. Here, we introduce the R packageparallelMCMCcombine which carries out four of these techniques for combiningindependent subset posterior samples. We illustrate each of the methods using aBayesian logistic regression model for simulation data and a Bayesian Gammamodel for real data; we also demonstrate features and capabilities of the Rpackage. The package assumes the user has carried out the Bayesian analysis andhas produced the independent subposterior samples outside of the package. Themethods are primarily suited to models with unknown parameters of fixeddimension that exist in continuous parameter spaces. We envision this tool willallow researchers to explore the various methods for their specificapplications, and will assist future progress in this rapidly developing field.

parallelMCMCcombine: An R Package for Bayesian Methods for Big Data and Analytics