fmou-class: FMOU class

In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

FMOU class

Description

An S4 class for fast parameter estimation in the FMOU model, a latent factor model with a fixed or estimated orthogonal factor loading matrix, where each latent factor is modeled as an O-U (Ornstein-Uhlenbeck) process.

Objects from the Class

Objects of this class are created and initialized using the fmou function to set up the estimation.

Slots

output:

object of class matrix. The observation matrix.

d

object of class integer to specify the number of latent factors.

est_d

object of class logical, default is FALSE. If TRUE, d will be estimated by either variance matching (when noise level is given) or information criteria (when noise level is unknown). Otherwise, d is fixed, and users must assign a value to d.

est_U0

object of class logical, default is TRUE. If TRUE, the factor loading matrix (U0) will be estimated. Otherwise, U0 is fixed.

est_sigma0_2

object of class logical, default is TRUE . If TRUE, the variance of the noise will be estimated. Otherwise, it is fixed.

U0

object of class matrix. The fixed factor loading matrix. Users should assign a k*d matrix to it when est_U0=False. Here k is the length of observations at each time step.

sigma0_2

object of class numeric. Variance of noise. User should assign a value to it when est_sigma0_2=False.

Methods

fit.fmou

See fit.fmou.

predict.fmou

See predict.fmou.

Author(s)

Mengyang Gu [aut, cre], Xinyi Fang [aut], Yizi Lin [aut]

Maintainer: Mengyang Gu <[e-mail]>

References

Lin, Y., Liu, X., Segall, P., & Gu, M. (2025). Fast data inversion for high-dimensional dynamical systems from noisy measurements. arXiv preprint arXiv:2501.01324.

See Also

fmou for more details about how to create a fmou object.

FastGaSP documentation built on April 4, 2025, 5:16 a.m.