Issue #60 Theory Edwin Dong, Anurag Shinde, Ebrahim Jahangiri
An Introduction To Gaussian Process Regression
Free editionPRO edition
Welcome to PRO! This week’s issue is accompanied by a Colab notebook, going over a basic implementation of a Gaussian Process Regression. The code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
Feel free to play around with the code and learn more about how GPR works! You can find the notebook here.
Gaussian Process Regression (GPR) is a method used to make predictions by calculating the probability of the possible outcomes based on observed data.
To understand how and why this approach of probabilistic prediction is so useful, we first need to explore the fundamentals of regression techniques. So, what is a Gaussian (distribution), and how is it used in Gaussian Process Regression?
This edition is for PRO members.
