Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm
Abstract
A random-walk Metropolis sampler is geometrically ergodic if its equilibrium density is super-exponentially light and satisfies a curvature condition [Stochastic Process. Appl. 85 (2000) 341-361]. Many applications, including Bayesian analysis with conjugate priors of logistic and Poisson regression and of log-linear models for categorical data result in posterior distributions that are not super-exponentially light. We show how to apply the change-of-variable formula for diffeomorphisms to obtain new densities that do satisfy the conditions for geometric ergodicity. Sampling the new variable and mapping the results back to the old gives a geometrically ergodic sampler for the original variable. This method of obtaining geometric ergodicity has very wide applicability.
Cite
@article{arxiv.1302.6741,
title = {Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm},
author = {Leif T. Johnson and Charles J. Geyer},
journal= {arXiv preprint arXiv:1302.6741},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.1214/12-AOS1048 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org). With Corrections