NuggetKriging::simulate

Description

Simulation from a NuggetKriging model object.

Usage

  • Python

    # k = NuggetKriging(...)
    k.predict(nsim = 1, seed = 123, x)
    
  • R

    # k = NuggetKriging(...)
    k$predict(nsim = 1, seed = 123, x)
    
  • Matlab/Octave

    % k = NuggetKriging(...)
    k.predict(nsim = 1, seed = 123, x)
    

Arguments

Argument

Description

nsim

Number of simulations to perform.

seed

Random seed used.

x

Points in model input space where to simulate.

Details

This method draws paths of the stochastic process at new input points conditional on the values at the input points used in the fit.

Value

a matrix with length(x) rows and nsim columns containing the simulated paths at the inputs points given in x .

Examples

f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
plot(f)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X) + 0.1  *rnorm(nrow(X))
points(X, y, col = "blue")

k <- NuggetKriging(y, X, kernel = "matern3_2")

x <- seq(from = 0, to = 1, length.out = 101)
s <- k$simulate(nsim = 3, x = x)

lines(x, s[ , 1], col = "blue")
lines(x, s[ , 2], col = "blue")
lines(x, s[ , 3], col = "blue")

Results


Reference