MCMC Ensemble Sampler

Ensemble Markov Chain Monte Carlo samplers with different strategies to generate proposals. Either the stretch move as proposed by Goodman and Weare (2010), or a differential evolution jump move (similar to Braak and Vrugt, 2008) is used.

Installation

1. Install the R package devtools
2. Then run from R

library(devtools)
install_github("SandaD/MCMCEnsembleSampler")

Usage

library(MCMCEnsembleSampler)

## a log-pdf to sample from
p.log <- function(x) {
    B <- 0.03                              # controls 'bananacity'
    -x[1]^2/200 - 1/2*(x[2]+B*x[1]^2-100*B)^2
}

## use stretch move
res1 <- MCMCEnsemble(p.log, lower.inits=c(a=0, b=0), upper.inits=c(a=1, b=1),
                     max.iter=3000, n.walkers=10, method="s")
str(res1)


## use stretch move, return samples as 'coda' object
res2 <- MCMCEnsemble(p.log, lower.inits=c(a=0, b=0), upper.inits=c(a=1, b=1),
                     max.iter=3000, n.walkers=10, method="s", coda=TRUE)

summary(res2$samples)
plot(res2$samples)


## use different evolution move, return samples as 'coda' object
res3 <- MCMCEnsemble(p.log, lower.inits=c(a=0, b=0), upper.inits=c(a=1, b=1),
                     max.iter=3000, n.walkers=10, method="d", coda=TRUE)

summary(res3$samples)
plot(res3$samples)

References

Goodman, J. and Weare, J. (2010) Ensemble samplers with affine invariance. Communications in Applied Mathematics and Computational Science, 5(1), 65–80.

Braak, C. J. F. ter and Vrugt, J. A. (2008) Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435–446.