Marginal Gaussian Process

The Model

A Marginal Gaussian process jointly represents the data as a large probability distribution. This distribution, as we will see, turns out to a normal distribution in classical Bayesian approaches. Suppose we have some data \(X, y\) using which we want to predict the distribution over …

Kernels for GP Modelling in PyMC4.

In this tutorial, we will explore the covariance functions aka kernels present in pm.gp module and study their properties. This will help choose the appropriate kernel to model your data properly. We will also see the semantics of additive and multiplicative kernels.

import …

Theory

Gaussian processes are non-parametric models that define a distribution over a function where the function itself is a random variable of some inputs \(X\). They can be thought of as a distribution over infinite dimensions but computation can be done using finite resources. This property makes them useful for …