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 intractable distributions that arise in statistical
applications, which typically have uncountable support.