I've been studying up Gaussian process modeling for machine learning.
For someone seeing these concepts for the first time, I would recommend the following sequence based on my experience:
1. A Visual Exploration of Gaussian Processes
It hits the key points of what makes multinormal distributions special (conditionals and marginals are normal too!), and the visuals help build intuition.
1a. Gaussian Processes for Dummies
You might not need this, but I like this essay because it is jargon-free, and focuses on how to get things going. There is python code at the end, which you can play with.
2. Chapter 2 of Gaussian Process for Machine Learning
This "bible" is astonishingly well-written. If you are familiar with linear algebra and some statistics, this is a breezy read. Plus, all the important formulae and algorithms you see in different articles, are available here in one place!
3. If you like videos, then this YouTube lecture might be worth watching!
For someone seeing these concepts for the first time, I would recommend the following sequence based on my experience:
1. A Visual Exploration of Gaussian Processes
It hits the key points of what makes multinormal distributions special (conditionals and marginals are normal too!), and the visuals help build intuition.
1a. Gaussian Processes for Dummies
You might not need this, but I like this essay because it is jargon-free, and focuses on how to get things going. There is python code at the end, which you can play with.
2. Chapter 2 of Gaussian Process for Machine Learning
This "bible" is astonishingly well-written. If you are familiar with linear algebra and some statistics, this is a breezy read. Plus, all the important formulae and algorithms you see in different articles, are available here in one place!
3. If you like videos, then this YouTube lecture might be worth watching!
