FastGaSP (version 0.6.0)

fgasp: Setting up the Fast GaSP model

Description

Creating an fgasp class for a GaSP model with matern covariance.

Usage

fgasp(input, output, have_noise=TRUE, kernel_type='matern_5_2')

Value

fgasp returns an S4 object of class fgasp (see fgasp).

Arguments

input

a vector with dimension num_obs x 1 for the sorted input locations.

output

a vector with dimension n x 1 for the observations at the sorted input locations.

have_noise

a bool value. If it is true, it means the model contains 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.

Author

tools:::Rd_package_author("FastGaSP")

Maintainer: tools:::Rd_package_maintainer("FastGaSP")

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.

Examples

library(FastGaSP)

#-------------------------------------
# Example 1: a simple example with noise 
#-------------------------------------

y_R<-function(x){
  cos(2*pi*x)
}

###let's test for 2000 observations
set.seed(1)
num_obs=2000
input=runif(num_obs)

output=y_R(input)+rnorm(num_obs,mean=0,sd=0.1)

##constucting the fgasp.model
fgasp.model=fgasp(input, output)
show(fgasp.model)

#------------------------------------------
# Example 2: a simple example with no noise 
#------------------------------------------

y_R<-function(x){
  sin(2*pi*x)
}


##generate some data without noise
num_obs=50
input=seq(0,1,1/(num_obs-1))

output=y_R(input)


##constucting the fgasp.model
fgasp.model=fgasp(input, output,have_noise=FALSE)

show(fgasp.model)