MCMC.parallel: Parallel computation of MCMC()

In adaptMCMC: Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler

Description

A wrapper function to generate several independent Markov chains by stetting up cluster on a multi-core machine. The function is based on the parallel package.

Usage

MCMC.parallel(p, n, init, n.chain = 4, n.cpu, packages = NULL, dyn.libs=NULL,
    scale = rep(1, length(init)),  adapt = !is.null(acc.rate),
    acc.rate = NULL, gamma = 2/3, list = TRUE, ...)

Arguments

p	function that returns a value proportional to the log probability density to sample from. Alternatively the function can return a list with at least one element named log.density.
n	number of samples.
init	vector with initial values.
n.chain	number of independent chains.
n.cpu	number of CPUs that should be used in parallel.
packages	vector with name of packages to load into each instance. (Typically, all packages on which p depends.)
dyn.libs	vector with name of dynamic link libraries (shared objects) to load into each instance. The libraries must be located in the working directory.
scale	vector with the variances or covariance matrix of the jump distribution.
adapt	if TRUE, adaptive sampling is used, if FALSE classic metropolis sampling, if a positive integer the adaption stops after adapt iterations.
acc.rate	desired acceptance rate (ignored if adapt=FALSE)
gamma	controls the speed of adaption. Should be between 0.5 and 1. A lower gamma leads to faster adaption.
list	logical. If TRUE a list of lits is returned otherwise a list of matrices with the samples.
...	further arguments passed to p

Details

This function is just a wrapper to use MCMC in parallel. It is based on parallel. Obviously, the application of this function makes only sense on a multi-core machine.

Value

A list with a list or matrix for each chain. See MCMC for details.

Author(s)

Andreas Scheidegger, [e-mail] or [e-mail]

See Also

MCMC

Examples

## ----------------------
## Banana shaped distribution

## 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
}

## ----------------------
## generate samples
## compute 4 independent chains on 2 CPU's (if available) in parallel

samp <- MCMC.parallel(p.log, n=200, init=c(x1=0, x2=1),
    n.chain=4, n.cpu=2, scale=c(1, 0.1),
    adapt=TRUE, acc.rate=0.234)

str(samp)

adaptMCMC documentation built on March 29, 2021, 9:11 a.m.