Posts in reference
Gaussian Processes: Latent Variable Implementation
- 28 September 2022
- Category: reference, intermediate
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 \(f(x)\),
Mean and Covariance Functions
- 22 March 2022
- Category: reference, intermediate
A large set of mean and covariance functions are available in PyMC. It is relatively easy to define custom mean and covariance functions. Since PyMC uses Aesara, their gradients do not need to be defined by the user.