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# Spatio-Temporal Gaussian Process
# Squared Exponential Kernel
kernel <- function(x1, x2, l, sigma) {
d <- sqrt(sum((x1 - x2)^2))
sigma^2 * exp(-d^2 / (2 * l^2))
}
# Build covariance matrix
K <- outer(locations, locations,
Vectorize(kernel),
l = 0.5, sigma = 1.0)
# Sample from GP prior
f <- mvrnorm(n = 1,
mu = rep(0, n),
Sigma = K)Whether you have a specific analysis in mind or need help figuring out the right approach, I'm here to help. Reach out and let's talk about what you need.
