Posts tagged gaussian processes
gp.Latent class is a direct implementation of a Gaussian process without approximation. Given a mean and covariance function, we can place a prior on the function \(f(x)\),
gp.Marginal class implements the more common case of GP regression: the observed data are the sum of a GP and Gaussian noise.
gp.Marginal has a
marginal_likelihood method, a
conditional method, and a
predict method. Given a mean and covariance function, the function \(f(x)\) is modeled as,