predictobj.fgasp-class: Predictive results for the Fast GaSP class

In FastGaSP: Fast and Exact Computation of Gaussian Stochastic Process

Predictive results for the Fast GaSP class

Description

S4 class for prediction for a Fast GaSP model with or without a noise.

Objects from the Class

Objects of this class are created and initialized with the function predict that computes the prediction and the uncertainty quantification.

Slots

num_testing:

object of class integer. Number of testing inputs.

testing_input:

object of class vector. The testing input locations.

param

a vector of parameters. The first parameter is the natural logarithm of the inverse range parameter in the kernel function. If the data contain noise, the second parameter is the logarithm of the nugget-variance ratio parameter.

mean:

object of class vector. The predictive mean at testing inputs.

var:

object of class vector. The predictive variance at testing inputs. If the var_data is true, the predictive variance of the data is calculated. Otherwise, the predictive variance of the mean is calculated.

var_data:

object of class logical. If the var_data is true, the predictive variance of the data is calculated for var. Otherwise, the predictive variance of the mean is calculated for var.

Author(s)

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

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

predict.fgasp for more details about how to do prediction for a fgasp object.

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