Optimal scaling of the Random Walk Metropolis algorithm under Lp mean differentiability
Abstract
This paper considers the optimal scaling problem for high-dimensional random walk Metropolis algorithms for densities which are differentiable in Lp mean but which may be irregular at some points (like the Laplace density for example) and/or are supported on an interval. Our main result is the weak convergence of the Markov chain (appropriately rescaled in time and space) to a Langevin diffusion process as the dimension d goes to infinity. Because the log-density might be non-differentiable, the limiting diffusion could be singular. The scaling limit is established under assumptions which are much weaker than the one used in the original derivation of [6]. This result has important practical implications for the use of random walk Metropolis algorithms in Bayesian frameworks based on sparsity inducing priors.
Cite
@article{arxiv.1604.06664,
title = {Optimal scaling of the Random Walk Metropolis algorithm under Lp mean differentiability},
author = {Alain Durmus and Sylvain Le Corff and Eric Moulines and Gareth O. Roberts},
journal= {arXiv preprint arXiv:1604.06664},
year = {2016}
}