fgasp-class: Fast GaSP class

In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

Description

S4 class for fast computation 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 and initialized with the function fgasp that computes the calculations needed for setting up the estimation and prediction.

Slots

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.

Methods

show

Prints the main slots of the object.

predict

See predict.

Author(s)

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <[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.

See Also

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

FastGaSP documentation built on Sept. 5, 2021, 5:36 p.m.