simpleMH: Simple Metropolis-Hastings MCMC

In simpleMH: Simple Metropolis-Hastings MCMC Algorithm

Simple Metropolis-Hastings MCMC

Description

Simple Metropolis-Hastings MCMC

Usage

simpleMH(f, inits, theta.cov, max.iter, coda = FALSE, ...)

Arguments

f	function that returns a single scalar value proportional to the log probability density to sample from.
inits	numeric vector with the initial values for the parameters to estimate
theta.cov	covariance matrix of the parameters to estimate.
max.iter	maximum number of function evaluations
coda	logical. Should the samples be returned as coda::mcmc object? (defaults to FALSE)
...	further arguments passed to f

Value

• if coda = FALSE a list with:

  • samples: A two dimensional array of samples with dimensions generation x parameter

  • log.p: A numeric vector with the log density evaluate at each generation.

• if coda = TRUE a list with:

  • samples: A object of class coda::mcmc containing all samples.

  • log.p: A numeric vector with the log density evaluate at each generation.

Examples

p.log <- function(x) {
B <- 0.03
return(-x[1]^2/200 - 1/2*(x[2]+B*x[1]^2-100*B)^2)
}

simpleMH(p.log, inits=c(0, 0), theta.cov = diag(2), max.iter=3000)

simpleMH documentation built on May 29, 2024, 8:32 a.m.