In this paper, we shall optimize the efficiency of Metropolis algorithms for multidimensional target distributions with scaling terms possibly depending on the dimension. We propose a method for determining the appropriate form for the scaling of the proposal distribution as a function of the dimension, which leads to the proof of an asymptotic diffusion theorem. We show that when there does not exist any component with a scaling term significantly smaller than the others, the asymptotically optimal acceptance rate is the well-known 0.234.
"Weak convergence of Metropolis algorithms for non-i.i.d. target distributions." Ann. Appl. Probab. 17 (4) 1222 - 1244, August 2007. https://doi.org/10.1214/105051607000000096