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.
Citation
Laird Arnault Breyer. Mauro Piccioni. Sergio Scarlatti. "Optimal scaling of MaLa for nonlinear regression." Ann. Appl. Probab. 14 (3) 1479 - 1505, August 2004. https://doi.org/10.1214/105051604000000369
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