S4 class for fast computation of the Gaussian stochastic process (GaSP) model with the Matern kernel function with or without a noise.
Objects of this class are created and initialized with the function fgasp that computes the calculations needed for setting up the estimation and prediction.
num_obs:object of class integer. The number of experimental observations.
have_noise:object of class logical to specify whether the the model has a noise or not. "TRUE" means the model contains a noise and "FALSE" means the model does not contain a noise.
kernel_type:a character to specify the type of kernel to use.The current version supports kernel_type to be "matern_5_2" or "exp", meaning that the matern kernel with roughness parameter being 2.5 or 0.5 (exponent kernel), respectively.
input:object of class vector with dimension num_obs x 1 for the sorted input locations.
delta_x:object of class vector with dimension (num_obs-1) x 1 for the differences between the sorted input locations.
output:object of class vector with dimension num_obs x 1 for the observations at the sorted input locations.
Prints the main slots of the object.
See predict.
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.
fgasp for more details about how to create a fgasp object.