gpcov: Conditional covariance of Gaussian process given observations

In magi: MAnifold-Constrained Gaussian Process Inference

Conditional covariance of Gaussian process given observations

Description

Compute the conditional covariance of a Gaussian process, given a vector of observations, hyper-parameters phi, and noise standard deviation sigma.

Usage

gpcov(yobs, tvec, tnew, phi, sigma, kerneltype = "generalMatern")

Arguments

yobs	vector of observations
tvec	vector of time points corresponding to observations
tnew	vector of time points at which the conditional covariance should be computed
phi	vector of hyper-parameters for the covariance kernel (kerneltype)
sigma	the noise level (if known). By default, both phi and sigma are estimated. If a value for sigma is supplied, then sigma is held fixed at the supplied value and only phi is estimated.
kerneltype	the covariance kernel, types matern, rbf, compact1, periodicMatern, generalMatern are supported. See calCov for their definitions.

Value

The conditional covariance matrix for the GP evaluated at the time points in tnew.

Examples

# Load Fitzhugh-Nagumo dataset
data(FNdat)

tnew <- seq(15, 20, by = 0.5)

# GP covariance of V component at time points in tnew given observations
gpcov(FNdat$V, FNdat$time, tnew, c(2.3, 1.2), 0.2)

magi documentation built on April 26, 2023, 1:12 a.m.