FastGaSP (version 0.5.2)

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')

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.

Value

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

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

# NOT RUN {
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)

# }