consensusMCindep

array of subset posterior samples of the dimension c(d,sampT,M). Here d is the dimension of the parameter space, sampT is the number of samples, and M is the number of subposterior datasets.

subchain

shuff: logical; if TRUE, each of the M subsets of d dimensional parameters in subchain is shuffled.

shuff

The function uses the Consensus Monte Carlo algorithm introduced by Scott et al. (see References) to combine the independent subset posterior samples subchains into the set of samples that estimate the posterior density given the full data set. The Consensus Monte Carlo algorithm uses a weighted average of the subset posterior samples to produce the combined posterior samples, where the weights are based on the inverse variance-covariance matrix of the subset posterior samples. Here, the model parameters are assumed to be independent, so that the covariance between model parameters is equal to zero.

combine

consensus

subposterior

posterior

parallel

See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. 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.

Erin Conlon

parallelMCMCcombine

Combining Subset MCMC Samples to Estimate a Posterior Density

consensusMCindep function

Consensus Monte Carlo Algorithm (for independent parameters) — consensusMCindep

Consensus Monte Carlo Algorithm (for independent parameters)

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.

consensusMCindep: Consensus Monte Carlo Algorithm (for independent parameters)

Description

Usage

Arguments

Value

Details

References

Examples