arXiv stat.ML
· Papers
Fast data inversion for high-dimensional Ornstein-Uhlenbeck processes from noisy measurements
arXiv:2501.01324v5 Announce Type: replace-cross Abstract: In this work, we develop a scalable approach for a flexible latent factor model for high-dimensional dynamical systems. Each latent factor process has its own correlation and variance parameters, and the orthogonal factor loading matrix can be either fixed or es