  Many applications in the field of statistics require Markov chain Monte Carlo
methods. Determining appropriate starting values and run lengths can be both
analytically and empirically challenging. A desire to overcome these problems
has led to the development of exact, or perfect, sampling algorithms which
convert a Markov chain into an algorithm that produces i.i.d. samples from the
stationary distribution. Unfortunately, very few of these algorithms have been
developed for the distributions that arise in statistical applications, which
typically have uncountable support. Here we study an exact sampling algorithm
using a geometrically ergodic Markov chain on a general state space. Our work
provides a significant reduction to the number of input draws necessary for the
Bernoulli factory, which enables exact sampling via a rejection sampling
approach. We illustrate the algorithm on a univariate Metropolis-Hastings
sampler and a bivariate Gibbs sampler, which provide a proof of concept and
insight into hyper-parameter selection. Finally, we illustrate the algorithm on
a Bayesian version of the one-way random effects model with data from a styrene
exposure study.
