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 nonoverlapping 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 PoissonGamma 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.
Author  Alexey Miroshnikov, Erin Conlon 
Date of publication  20140620 08:03:26 
Maintainer  Alexey Miroshnikov <amiroshn@gmail.com> 
License  GPL (>= 2) 
Version  1.0 
Package repository  View on CRAN 
Installation  Install the latest version of this package by entering the following in R:



All man pages Function index File listing
Man pages  

consensusMCcov: Consensus Monte Carlo Algorithm (for correlated parameters)  
consensusMCindep: Consensus Monte Carlo Algorithm (for independent parameters)  
parallelMCMCcombinepackage: parallelMCMCcombine  
sampleAvg: Sample Averaging Method  
semiparamDPE: Semiparametric Consensus Method 
Functions 

Files  

NAMESPACE
 
R
 
R/semiparamDPE.R  
R/consensusMCindep.R  
R/sampleAvg.R  
R/consensusMCcov.R  
MD5
 
DESCRIPTION
 
man
 
man/consensusMCcov.Rd  
man/sampleAvg.Rd  
man/parallelMCMCcombinepackage.Rd  
man/semiparamDPE.Rd  
man/consensusMCindep.Rd 
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