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Condition a Gaussian Process

Source: R/gp_functions.R
condition_gaussian_process.Rd

[Experimental]

Condition a Multivariate Normal distribution based on a Gaussian Process using a exponentiated quadratic covariance matrix.

Usage

condition_gaussian_process(
  n,
  x,
  y,
  x_new,
  amplitude = 1,
  length_scale = 1,
  delta = 1e-09
)

Arguments

n

number of random draws.

x

position of values to condition on.

y

values to condition on (at positions x).

x_new

new positions to estimate values at.

amplitude

Vertical scale of the covariance function

length_scale

Horizontal scale of the covariance function

delta

A small offset along the diagonal of the resulting covariance matrix to ensure the function returns a positive-semidefinite matrix. Can also be used as a white noise kernel to allow for increased variation at individual positions along the vector x.

Value

A matrix of size \(M \times N\), where \(M\) is length(x_new), and \(N\) is n. Each column is a random draw from the conditioned multivariate normal where each row corresponds to the draw at the position in x_new.

Examples

x <- 1:10
y <- rep(0, 10)
x_new <- 11:15

condition_gaussian_process(3, x, y, x_new)
#>            [,1]       [,2]       [,3]
#> [1,]  0.8364084 -0.3599454 -0.2408916
#> [2,]  0.6685950 -0.5781517  0.8602529
#> [3,]  0.2979424 -0.6303187  1.1717011
#> [4,] -0.4289284  0.7481498  0.2505266
#> [5,]  0.8509445  0.3492500  0.5947322