• Pattern Recognition and Machine Learning - Chapter 6.4
• Bayesian Data Analysis 3rd Edition - Chapter 21
• Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences
• Machine Learning: a Probabilistic Perspective - Chapter 15
• Applied Stochastic Differential Equations
• Gaussian Processes for Machine Learning

Thesis

• Automatic Model Construction with Gaussian Processes
• Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian processes

Other resources

• Gaussian Process Model Zoo

Introductory

• A Visual Exploration of Gaussian Processes
• Gaussian process introductory tutorial in Python¶
• Gaussian Processes, not quite for dummies
• Robust Gaussian Process Modeling
• The Kernel Cookbook
• Interactive Gaussian Process Visualization

Applications

• Gaussian process demonstration with Stan
• Gaussian Process Classification Model in various PPLs
• Exploring Bayesian Optimization
• Random effects in Gaussian Processes

Applications

• Gaussian Process Summer Schools
• Gaussian Process Basics
• Gaussian Processes
• Introduction to Gaussian processes
• MLSS 2013 Tübingen
• Part 1
• MLSS 2015 Tübingen
• Part 1
• Part 1
• Machine Learning with Signal Processing (ICML 2020 Tutorial)
• Gaussian processes for fun and profit: Probabilistic machine learning in industry
• A Primer on Gaussian Processes for Regression Analysis | PyData NYC 2019
• Open Data Science Initiative
• Introduction to Gaussian processes
• Part 1
• Part 1
• Part 1
• Machine Learning with Signal Processing (ICML 2020 Tutorial)
• Gaussian processes for fun and profit: Probabilistic machine learning in industry
• A Primer on Gaussian Processes for Regression Analysis | PyData NYC 2019

Python

• celerite
• GPyTorch
• BoTorch
• scikit-learn GP module
• PyMC3
• Pyro
• GPy
• GPflow

Julia

• GaussianProcesses.jl

Stan

• Stan User's Guide - Gaussian Processes chapter

Octave / Matlab

• GPstuff
• GPML toolbox

Bayesian Optimization

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• Taking the Human Out of the Loop: A Review of Bayesian Optimization

Causality

• Causal Inference using Gaussian Processes with Structured Latent Confounders

Multiple-output Gaussian processes

• Kernels for Vector-Valued Functions: a Review

Survival Analysis

• Gaussian Processes for Survival Analysis

Time Series

• Gaussian processes for time-series modelling

Time Series

• Gaussian Processes Cambridge