Self Regenerative Markov Chain Monte Carlo

Abstract

this article we propose a new algorithm, called SR (Self Regenerative), with a different philosophy for MCMC computations. Given a draw from the proposal density we compute how many times we want to keep the proposed point in the sample. This is a draw from the geometric distribution with an appropriate success probability. Once this has been performed we go on to simulate another independent candidate point from the proposal distribution and iterate. Example 1. Consider the target distribution ß(x) =