`Kriging::simulate`

Description

Simulate from a Kriging Model Object.

Usage

Python

# k = Kriging(...)
k.simulate(nsim = 1, seed = 123, x = x, will_update = False)

R

# k = Kriging(...)
k$simulate(nsim = 1, seed = 123, x = x, will_update = FALSE)

Matlab/Octave

% k = Kriging(...)
k.simulate(nsim = 1, seed = 123, x = x, will_update = false)

Julia

# k = Kriging(...)
s = simulate(k, nsim=1, seed=123, x=x, will_update=false)

Arguments

Argument	Description
`nsim`	Number of simulations to perform.
`seed`	Random seed used.
`x`	Points in model input space where to simulate.
`will_update`	Set to `TRUE` if you plan to call `update_simulate(...)` afterwards.

Details

This method draws \(n_{\texttt{sim}}\) conditional paths of the latent process at the new input points.

The exact simulation mode follows the fitted noise strategy: noise = NULL gives interpolation paths, noise = "nugget" uses the nugget model, and noise = <variance vector> uses the heteroskedastic model. Set will_update = TRUE to cache the simulation state for a later update_simulate(...) call.

Value

A matrix with nrow(x) rows and nsim columns containing the simulated paths at the input 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")

Kriging::simulate