On directional Metropolis–Hastings algorithms

Abstract

New Metropolis–Hastings algorithms using directional updates are introduced in this paper. Each iteration of a directional Metropolis–Hastings algorithm consists of three steps (i) generate a line by sampling an auxiliary variable, (ii) propose a new state along the line, and (iii) accept/reject according to the Metropolis–Hastings acceptance probability. We consider two classes of directional updates. The first uses a point in ${\cal R}$ ⁿ as auxiliary variable, the second an auxiliary direction vector. The proposed algorithms generalize previous directional updating schemes since we allow the distribution of the auxiliary variable to depend on properties of the target at the current state. By letting the proposal distribution along the line depend on the density of the auxiliary variable, we identify proposal mechanisms that give unit acceptance rate. When we use direction vector as auxiliary variable, we get the advantageous effect of large moves in the Markov chain and hence the autocorrelation length of the samples is small. We apply the directional Metropolis–Hastings algorithms to a Gaussian example, a mixture of Gaussian densities, and a Bayesian model for seismic data.