Abstract
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.
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