JacobRaymond/ConquerMCMC: Divide and Conquer Algorithms for Markov Chain Monte Carlo
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.
Getting started
Browse package contents
Package details |
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| Maintainer | |
| License | GPL-3 |
| Version | 0.0.0.9000 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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