| Name : R-parallelMCMCcombine
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| Version : 2.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.1.12
| Date : 2024-06-14 11:22:10
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| Group : Development/Libraries/Other
| Source RPM : R-parallelMCMCcombine-2.0-lp153.1.12.src.rpm
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| Size : 0.04 MB
| |
| Packager : (none)
| |
| Summary : Combining Subset MCMC Samples to Estimate a Posterior Density
|
Description :
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.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
