Optimal scaling of MaLa for nonlinear regression
Optimal scaling of MaLa for nonlinear regression
We address the problem of simulating efficiently from the posterior distribution over the parameters of a particular class of nonlinear regression models using a Langevin-Metropolis sampler. It is shown that as the number N of parameters increases, the proposal variance must scale as N{-1/3} in order to converge to a …