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parallelMCMCcombine
Combining Subset MCMC Samples to Estimate a Posterior Density
View on CRAN: Click
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Download and install parallelMCMCcombine package within the R console
Install from CRAN:
install.packages("parallelMCMCcombine")
Install from Github:
library("remotes")
install_github("cran/parallelMCMCcombine") Install by package version:
library("remotes")
install_version("parallelMCMCcombine", "2.0") Attach the package and use:
library("parallelMCMCcombine")
Maintained by
Erin Conlon
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[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.parallelMCMCcombine . parallelMCMCcombine results . parallelMCMCcombine.pdf . parallelMCMCcombine_2.0.tar.gz . parallelMCMCcombine_2.0.zip . parallelMCMCcombine_2.0.zip . parallelMCMCcombine_2.0.zip . parallelMCMCcombine_2.0.tgz . parallelMCMCcombine_2.0.tgz . parallelMCMCcombine_2.0.tgz . parallelMCMCcombine_2.0.tgz . parallelMCMCcombine_2.0.tgz . parallelMCMCcombine_2.0.tgz . parallelMCMCcombine archive . https://CRAN.R-project.org/package=parallelMCMCcombine .
First Published: 2014-06-20
Latest Update: 2021-06-23
Description:
See Miroshnikov and Conlon (2014) . Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
How to cite:
Erin Conlon (2014). parallelMCMCcombine: Combining Subset MCMC Samples to Estimate a Posterior Density. R package version 2.0, https://cran.r-project.org/web/packages/parallelMCMCcombine. Accessed 29 Mar. 2025.
Previous versions and publish date:
1.0 (2014-06-20 08:03)
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Complete documentation for parallelMCMCcombine
Functions, R codes and Examples using
the parallelMCMCcombine R package
Some associated functions: consensusMCcov . consensusMCindep . parallelMCMCcombine-package . sampleAvg . semiparamDPE .
Some associated R codes: consensusMCcov.R . consensusMCindep.R . sampleAvg.R . semiparamDPE.R . Full parallelMCMCcombine package functions and examples
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