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 of parameters increases, the proposal variance must scale in a precise way in order to converge to a diffusion. This generalizes previous results of Roberts and Rosenthal, showing the robustness of their analysis.
Optimal scaling of Metropolis adjusted Langevin algorithms for nonlinear regression / Breyer, L. A.; Piccioni, Mauro; Scarlatti, S.. - In: THE ANNALS OF APPLIED PROBABILITY. - ISSN 1050-5164. - STAMPA. - 14:(2004), pp. 1479-1505. [10.1214/105051604000000369]
Optimal scaling of Metropolis adjusted Langevin algorithms for nonlinear regression
PICCIONI, MAURO;
2004
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 of parameters increases, the proposal variance must scale in a precise way in order to converge to a diffusion. This generalizes previous results of Roberts and Rosenthal, showing the robustness of their analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
