adaptive.metropolis.sample: Adaptive Metropolis

In SamplerCompare: A Framework for Comparing the Performance of MCMC Samplers

Adaptive Metropolis

Description

Generate a sample from a probability distribution with the Adaptive Metropolis algorithm

Usage

adaptive.metropolis.sample(target.dist, x0, sample.size,
                           tuning=0.1, beta=0.05, burn.in=0.2)

Arguments

target.dist	Target distribution; see make.dist.
x0	Numeric vector containing initial state.
sample.size	Requested sample size.
tuning	Standard deviation of first component of proposal distribution
beta	Weight of first component of proposal distribution
burn.in	Stop adaptation after this fraction of the chain. Set this to 1.0 to obtain the behavior described by Roberts and Rosenthal (2009).

Details

This function implements the Adaptive Metropolis algorithm as described by Roberts and Rosenthal (2009). Proposals are a mixture of a spherical Gaussian with standard deviation equal to tuning/sqrt(target.dist$ndim) (with weight beta) and a Gaussian with covariance equal to the sample covariance of the already-computed observations scaled by 2.38^2/target.dist$ndim (with weight 1-beta). The resulting Markov chain is not strictly stationary with the target distribution for the burn-in period of the chain, but is ergodic.

Value

A list containing the elements X, evals, reject.rate, and sample.cov. This sampler follows the calling convention of compare.samplers. reject.rate contains the fraction of proposals that were rejected. sample.cov is the most recent sample covariance used to update the proposal distribution.

References

Roberts, G. O. and Rosenthal, J. S. (2009), “Examples of Adaptive MCMC,” Journal of Computational and Graphical Statistics 18(2):349-367.

See Also

compare.samplers, multivariate.metropolis.sample

SamplerCompare documentation built on April 24, 2023, 9:09 a.m.