Fast data inversion for high-dimensional Ornstein-Uhlenbeck processes from noisy measurements
含噪测量下高维 Ornstein–Uhlenbeck 过程的快速数据反演
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 estimated. We utilize an orthogonal factor loading matrix that avoids computing the inversion of the posterior covariance matrix at each time of the Kalman filter, and derive closed-form expressions in an…