Estimated_GP_params-class {SKFCPD} | R Documentation |
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 <hanmo@pstat.ucsb.edu>
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.