.png){kind=icon}
Return
CONSISTENT INFERENCE FOR DIFFUSIONS FROM LOW FREQUENCY MEASUREMENTS
.png){kind=icon}
OA
AI
DOI：10.1214/24-AOS2357.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Let (X-t) be a reflected diffusion process in a bounded convex domain in R-d, solving the stochastic differential equation dX(t) = del f(X-t)dt+root 2f(X-t)dW(t), t >= 0, with W-t a d-dimensional Brownian motion. The data X-0, X-D, ..., X-ND consist of discrete measurements and the time interval D between consecutive observations is fixed so that one cannot 'zoom' into the observed path of the process. The goal is to infer the diffusivity f and the associated transition operator P-t,P-f. We prove injectivity theorems and stability inequalities for the maps f bar right arrow P-t, f bar right arrow P-D,P-f, t
Keywords:
Bayesian inverse problems
reflected diffusion process
spectral PCA
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
Organization
Cited Papers
ShareSave
ShareSaveSTATISTICAL GUARANTEES FOR BAYESIAN UNCERTAINTY QUANTIFICATION IN NONLINEAR INVERSE PROBLEMS WITH GAUSSIAN PROCESS PRIORS.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSave
View more