Predict from a NoiseKriging Model Object


  • Python

    # k = NoiseKriging(...)
    k.predict(x, stdev = True, cov = False, deriv = False)
  • R

    # k = NoiseKriging(...)
    k$predict(x, stdev = TRUE, cov = FALSE, deriv = FALSE)
  • Matlab/Octave

    % k = NoiseKriging(...)
    k.predict(x, stdev = true, cov = false, deriv = false)





Input points where the prediction must be computed.


Logical . If TRUE the standard deviation is returned.


Logical . If TRUE the covariance matrix of the predictions is returned.


Logical . If TRUE the derivatives of mean and sd of the predictions are returned.


Given \(n^\star\) “new” input points \(\mathbf{x}^\star_{j}\), the method compute the expectation, the standard deviation and (optionally) the covariance of the estimated values of the “trend \(+\) GP” stochastic process \(\mu(\mathbf{x}_j^\star) + \zeta(\mathbf{x}_j^\star)\) at the “new” observations. The estimation is based on the distribution conditional on the \(n\) noisy observations \(y_i\) made at the input points \(\mathbf{x}_i\) as used when fitting the model. The \(n^\star\) input vectors (with length \(d\)) are given as the rows of a \(\mathbf{X}^\star\) corresponding to x.

The computation of these quantities is often called Universal Kriging see here for more details.


A list containing the element mean and possibly stdev and cov.

  • The expectation in mean is the estimate of the vector \(\textsf{E}[\boldsymbol{\mu}^\star + \boldsymbol{\zeta}^\star \, \vert \,\mathbf{y}]\) with length \(n^\star\) where \(\boldsymbol{\mu}^\star\) and \(\boldsymbol{\zeta}^\star\) are for “new” points and \(\mathbf{y}\) corresponds to the observations. Similarly the conditional standard deviation in stdev is a vector with length \(n^\star\) and the conditional covariance in cov is a \(n^\star \times n^\star\) matrix.

  • The (optional) derivatives are two \(n^\star \times d\) matrices pred_mean_deriv and pred_sdtdev_deriv with their row \(j\) containing the vector of derivatives w.r.t. to the new input point \(\mathbf{x}^\star\) evaluated at \(\mathbf{x}^\star = \mathbf{x}^\star_j\). So the row \(j\) of pred_mean_deriv contains the derivative \(\partial_{\mathbf{x}^\star} \mathbb{E}[y(\mathbf{x}^\star) \, \vert \,\mathbf{y}]\). evaluated at \(\mathbf{x}^\star = \mathbf{x}^\star_j\).

Note that for a NoiseKriging object the prediction is actually a smoothing. The so-called Kriging mean function \(\mathbf{x}^\star \mapsto \mathbb{E}[y(\mathbf{x}^\star) \, \vert \, \mathbf{y}]\) is a smooth function. Depending on the given noise variances \(\sigma^2_i\) given in the fit step, the prediction at \(\mathbf{x}^\star \approx \mathbf{x}_i\) will be more or less close to the observed value \(y_i\). As opposed to the NuggetKriging model case, duplicated inputs can be used in the design.


f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
X <- as.matrix(runif(10))
y <- f(X) + X/10 * rnorm(nrow(X))
points(X, y, col = "blue", pch = 16)

k <- NoiseKriging(y, (X/10)^2, X, "matern3_2")

x <-seq(from = 0, to = 1, length.out = 101)
p <- k$predict(x)

lines(x, p$mean, col = "blue")
polygon(c(x, rev(x)), c(p$mean - 2 * p$stdev, rev(p$mean + 2 * p$stdev)),
border = NA, col = rgb(0, 0, 1, 0.2))