Estimated_GP_params-class: Estimated GaSP parameters class

In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data

Estimated GaSP parameters class

Description

S4 class for fast parameter estimation of the Gaussian stochastic process (GaSP) model with the Matern kernel function with or without a noise.

Objects from the Class

Objects of this class are created with the function Estimate_GP_params that computes the calculations needed for setting up the estimation and prediction.

Slots

beta:

object of class numeric for the inverse of the range parameter, i.e. beta = 1/gamma.

eta:

object of class numeric for the estimated noise-to-signal parameter.

sigma_2:

object of class numeric for the estimated variance parameter.

Author(s)

Hanmo Li [aut, cre], Yuedong Wang [aut], Mengyang Gu [aut]

Maintainer: Hanmo Li <[e-mail]>

References

Hartikainen, J. and Sarkka, S. (2010). Kalman filtering and smoothing solutions to temporal gaussian process regression models, Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop, 379-384.

M. Gu, Y. Xu (2017), Nonseparable Gaussian stochastic process: a unified view and computational strategy, arXiv:1711.11501.

M. Gu, X. Wang and J.O. Berger (2018), Robust Gaussian Stochastic Process Emulation, Annals of Statistics, 46, 3038-3066.

SKFCPD documentation built on June 22, 2024, 11:06 a.m.