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FastGaSP (version 0.5.2)

Fast and Exact Computation of Gaussian Stochastic Process

Description

Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise. See the reference: Mengyang Gu and Yanxun Xu (2017), .

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Install

install.packages('FastGaSP')

Monthly Downloads

267

Version

0.5.2

License

GPL (>= 2)

Maintainer

Mengyang Gu

Last Published

September 2nd, 2021

Functions in FastGaSP (0.5.2)

Setting up the Fast GaSP model

Show an fgasp object.

Fast GaSP class

Sample the posterior distribution of the process using the backward smoothing algorithm

Natural logarithm of profile likelihood by the fast computing algorithm

Sample the prior process using a dynamic linear model

Kalman_smoother

the predictive mean and predictive variance by Kalman Smoother

Construct_W0_matern_5_2

covariance of the stationary distribution of the state when kernel is the Matern covariance with roughness parameter 2.5.

Prediction and uncertainty quantification on the testing input using a GaSP model.

predictobj.fgasp-class

Predictive results for the Fast GaSP class

Construct_G_matern_5_2

The coefficient matrix in the dynamic linear model when kernel is the Matern covariance with roughness parameter 2.5.

Construct_W_exp

The conditional covariance matrix of the state in the dynamic linear model when kernel is the exponential covariance

the natural logarithm of the determinant of the correlation matrix and the estimated sum of squares in the exponent of the profile likelihood

Construct_W0_exp

covariance of the stationary distribution of the state when kernel is the exponential covariance.

vector of half of the sum of squares

FastGaSP-package

Construct_W_matern_5_2

The conditional covariance matrix for matern covariance with roughness parameter 2.5

Construct_G_exp

The coefficient matrix in the dynamic linear model when kernel is the exponential covariance

matrices and vectors for the inverse covariance in the predictive distribution