Gaussian processes (1/3) - From scratch

This post explores some concepts behind Gaussian processes, such as stochastic processes and the kernel function. We will build up deeper understanding of Gaussian process regression by implementing them from scratch using Python and NumPy. This post is followed by a second post demonstrating how to fit a Gaussian process kernel with TensorFlow probability . In what follows we assume familiarity with basic probability and linear algebra especially in the context of multivariate Gaussian distributions. Have a look at this post if you need a refresher on the Gaussian distribution.