A Class Coupler for Perfect Sampling from Continuous Distributions With and Without Atoms
Methodology
2012-02-02 v1 Computation
Abstract
We consider the simulation of distributions that are a mixture of discrete and continuous components. We extend a Metropolis-Hastings-based perfect sampling algorithm of Corcoran and Tweedie to allow for a broader class of transition candidate densities. The resulting algorithm, know as a "class coupler", is fast to implement and is applicable to purely discrete or purely continuous densities as well. Our work is motivated by the study of a composite hypothesis test in a Bayesian setting via posterior simulation and we give simulation results for some problems in this area.
Cite
@article{arxiv.1202.0078,
title = {A Class Coupler for Perfect Sampling from Continuous Distributions With and Without Atoms},
author = {Wenjin Mao and Jem Corcoran},
journal= {arXiv preprint arXiv:1202.0078},
year = {2012}
}
Comments
21 pages, 9 figures; Journal of Statistical Theory and Applications Volume 10, Number 3, 2011