Pseudo-perfect and adaptive variants of the Metropolis-Hastings algorithm with an independent candidate density
We describe and examine an imperfect variant of a perfect sampling algorithm based on the Metropolis Hastings algorithm that appears to perform better than a more traditional approach in terms of speed and accuracy. We then describe and examine an 'adaptive' Metropolis - Hastings algorithm which generates and updates a self-target candidate density in such a way that there is no 'wrong choice' for an initial candidate density. Simulation examples are provided.
metropolis-hastings perfect sampling invariant measures backwards coupling coupling from the past exact sampling