# Gaussian Process Source: https://www.youtube.com/watch?v=xBE8qdAAj3w github: https://github.com/fonnesbeck/gp_regression Chris Fonnesbeck / New York Yankees ## 1. Regression models: the basics Gaussian process is a Beysian technique. Real data is very messy and non-linear unlike textbook examples of regression. Gaussian process regressions work very well for that. ![[Bayesian Inference]] 1. Introduction to probabilistic modeling - How can you model complex things using a Gaussian (normal) distributions? 2. What is a Gaussian process? - An overview of the features and properties of Gaussian processes. 3. Building Gaussian process models - Selecting your covariance function to suit your problem. 4. Fitting Gaussian process models - How you fit your GP depends on what you need it to do, and how much data you have. 5. Model checking and prediction - Does my GP work as advertised? What can I do with it?