Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics. (arXiv:2104.06384v3 [stat.ME] UPDATED)

High-dimensional limit theorems have been shown useful to derive tuning rules
for finding the optimal scaling in random-walk Metropolis algorithms. The
assumptions under which weak convergence results are proved are however
restrictive: the target density is typically assumed to be of a product form.
Users may thus doubt the validity of such tuning rules in practical
applications. In this paper, we shed some light on optimal-scaling problems
from a different perspective, namely a large-sample one. This allows to prove
weak …

algorithms arxiv bayesian random random-walk scaling