Adaptive Metropolis–Hastings methods

Abstract

This paper presents a method for adaptation in Metropolis–Hastings algorithms. A product of a proposal density and K copies of the target density is used to define a joint density which is sampled by a Gibbs sampler including a Metropolis step. This provides a framework for adaptation since the current value of all K copies of the target distribution can be used in the proposal distribution.