ConquerMCMC-package: ConquerMCMC: Divide and Conquer Algorithms for Markov Chain...
In JacobRaymond/ConquerMCMC: Divide and Conquer Algorithms for Markov Chain Monte Carlo
Description
A series of implementations of divide-and-conquer algorithms for Markov-Chain Monte Carlo. These functions provide scalable Metropolis-Hastings samplers to deal with large and disjointed data sets. Three methods, excluding the standard MCMC scheme, are available: the consensus MCMC, the m-posterior, and the scaled subprior. See function descriptions for proper attributions.
Author(s)
Maintainer: Jacob Raymond j4raymond@uwaterloo.ca
JacobRaymond/ConquerMCMC documentation built on May 12, 2020, 1:03 a.m.
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Description
A series of implementations of divide-and-conquer algorithms for Markov-Chain Monte Carlo. These functions provide scalable Metropolis-Hastings samplers to deal with large and disjointed data sets. Three methods, excluding the standard MCMC scheme, are available: the consensus MCMC, the m-posterior, and the scaled subprior. See function descriptions for proper attributions.
Author(s)
Maintainer: Jacob Raymond j4raymond@uwaterloo.ca
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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