Gaussian Processes: A Gentle Introduction

This blog post provides an accessible introduction to Gaussian processes (GPs), a powerful tool in machine learning. Starting with the fundamentals of multivariate Gaussian distributions, it explains marginalization and conditioning, leading to the core concept of GPs: predicting data by incorporating prior knowledge. Interactive figures and practical examples illustrate how GPs use kernel functions to define covariance matrices, controlling the shape of the predicted function. Bayesian inference updates the model with training data, allowing for prediction of function values and their confidence intervals.