Optimal Scaling of Mala for Nonlinear Regression
Probability
2007-05-23 v1
Abstract
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 diffusion. This generalizes previous results of Roberts and Rosenthal [J. R. Stat. Soc. Ser. B Stat. Methodol. 60 (1998) 255-268] for the i.i.d. case, showing the robustness of their analysis.
Cite
@article{arxiv.math/0407132,
title = {Optimal Scaling of Mala for Nonlinear Regression},
author = {Laird Arnault Breyer and Mauro Piccioni and Sergio Scarlatti},
journal= {arXiv preprint arXiv:math/0407132},
year = {2007}
}