Kriging::simulate

Description

Simulate from a Kriging Model Object.

Usage

  • Python

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

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

    % k = Kriging(...)
    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 \(n_{\texttt{sim}}\) paths of the stochastic process \(y(\mathbf{x})\) at the \(n^\star\) given new input points \(\mathbf{x}^\star_j\) conditional on the values \(y(\mathbf{x}_i)\) 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)
points(X, y, col = "blue")

k <- Kriging(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