Posts tagged gaussian processes
Gaussian Processes: Latent Variable Implementation
- 06 June 2023
- intermediate, reference
The 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 \(f(x)\),
Marginal Likelihood Implementation
- 04 June 2023
- intermediate, reference
The 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,