Kriging::update_simulate
Description
Update previous simulation of a Kriging
model object.
This method draws paths of the stochastic process conditional on the values at the input points used in the fit, plus the new input points and their values given as argument (knonw as ‘update’ points).
Usage
Python
# k = Kriging(...) # k.simulate(nsim = 1, seed = 123, x, will_update = True) k.update_simulate(y_u, X_u)
R
# k = Kriging(...) # k$simulate(nsim = 1, seed = 123, x, will_update = TRUE) k$update_simulate(y_u, X_u)
Matlab/Octave
% k = Kriging(...) % k.simulate(nsim = 1, seed = 123, x, will_update = true) k.update_simulate(y_u, X_u)
Arguments
Argument |
Description |
---|---|
|
Numeric vector of new responses (output). |
|
Numeric matrix of new input points. |
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, plus the new input points and their values given as argument (knonw as ‘update’ points).
Value
A matrix with nrow(x)
rows and nsim
columns containing the updated
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$simulate(nsim = 3, x = x, will_update = TRUE)
lines(x, s[ , 1], col = "blue")
lines(x, s[ , 2], col = "blue")
lines(x, s[ , 3], col = "blue")
X_u <- as.matrix(runif(3))
y_u <- f(X_u)
points(X_u, y_u, col = "red")
su <- k$update_simulate(y_u, X_u)
lines(x, su[ , 1], col = "blue", lty=2)
lines(x, su[ , 2], col = "blue", lty=2)
lines(x, su[ , 3], col = "blue", lty=2)
Results