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https://github.com/RaulPL/awesome-gaussian-processes

A curated list of resources for learning Gaussian Processes
https://github.com/RaulPL/awesome-gaussian-processes

List: awesome-gaussian-processes

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A curated list of resources for learning Gaussian Processes

Host: GitHub
URL: https://github.com/RaulPL/awesome-gaussian-processes
Owner: RaulPL
License: mit
Created: 2016-11-16T15:39:04.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2021-07-18T17:34:52.000Z (over 3 years ago)
Last Synced: 2024-08-08T18:02:27.992Z (3 months ago)
Size: 42 KB
Stars: 33
Watchers: 3
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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ultimate-awesome - awesome-gaussian-processes - A curated list of resources for learning Gaussian Processes. (Other Lists / PowerShell Lists)

README

        # Awesome Gaussian Processes

A list of resources for understanding Gaussian Processes. Inspired by [Awesome Normalizing Flows](https://github.com/janosh/awesome-normalizing-flows) list.




## Table of Contents

1. [📘 Books](#-books)

2. [🌐 Blog Posts](#-blog-posts)

3. [📺 Videos](#-videos)

4. [📦 Packages](#-packages)

5. [📝 Publications](#-publications)

6. [📌 Meetups](#-meetups)

7. [🎉 Open to Suggestions!](#-open-to-suggestions)




## 📘 Books

* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/)

* [Pattern Recognition and Machine Learning - Chapter 6.4](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)

* [Bayesian Data Analysis 3rd Edition - Chapter 21](http://www.stat.columbia.edu/~gelman/book/)

* [Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences](https://bookdown.org/rbg/surrogates/)

* [Machine Learning: a Probabilistic Perspective - Chapter 15](https://www.cs.ubc.ca/~murphyk/MLbook/)

* [Applied Stochastic Differential Equations](https://users.aalto.fi/~ssarkka/pub/sde_book.pdf)

### Thesis

* [Automatic Model Construction with Gaussian Processes](https://www.cs.toronto.edu/~duvenaud/thesis.pdf) by David K. Duvenaud

* [Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian processes](http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf) by Andrew G. Wilson

### Other resources

* [Gaussian Process Model Zoo](https://jejjohnson.github.io/gp_model_zoo/) by J. Emmanuel Johnson

## 🌐 Blog Posts

### Introductory

* [A Visual Exploration of Gaussian Processes](https://distill.pub/2019/visual-exploration-gaussian-processes/)

* [Gaussian process introductory tutorial in Python¶](http://adamian.github.io/talks/Damianou_GP_tutorial.html)

* [Gaussian Processes, not quite for dummies](https://thegradient.pub/gaussian-process-not-quite-for-dummies/)

* [Robust Gaussian Process Modeling](https://betanalpha.github.io/assets/case_studies/gaussian_processes.html)

* [The Kernel Cookbook](http://www.cs.toronto.edu/~duvenaud/cookbook/index.html)

* [Interactive Gaussian Process Visualization](http://www.infinitecuriosity.org/vizgp/)

### Applications

* [Gaussian process demonstration with Stan](https://avehtari.github.io/casestudies/Motorcycle/motorcycle_gpcourse.html) by Aki Vehtari

* [Gaussian Process Classification Model in various PPLs](https://luiarthur.github.io/TuringBnpBenchmarks/gpclassify)

* [Exploring Bayesian Optimization](https://distill.pub/2020/bayesian-optimization/)

* [Random effects in Gaussian Processes](https://martiningram.github.io/gp-random-effects/)

## 📺 Videos

* [Gaussian Process Summer Schools](http://gpss.cc/) 

* [Gaussian Process Basics](http://videolectures.net/gpip06_mackay_gpb/) by David MacKay

* [Gaussian Processes](http://videolectures.net/mlss09uk_rasmussen_gp/) by Carl E. Rasmussen

* [Introduction to Gaussian processes](https://youtu.be/4vGiHC35j9s) by  Nando de Freitas

* [ Open Data Science Initiative](https://www.youtube.com/channel/UCUjuEqUQbTrJ11f8nkWltQQ) channel

* [MLSS 2013 Tübingen](http://mlss.tuebingen.mpg.de/2013/index.html) GP Tutorial

  - [Part 1](https://youtu.be/50Vgw11qn0o), [Part 2](https://youtu.be/TR0LCVslIIM), [Part 3](https://youtu.be/KRLW5abMV6s)

* [MLSS 2015 Tübingen](http://mlss.tuebingen.mpg.de/2015/index.html) GP Tutorial

  - [Part 1](https://youtu.be/S9RbSCpy_pg), [Part 2](https://youtu.be/MxeQIKGEXb8), [Part 3](https://youtu.be/Ead4TivIOmU)

* MLSS 2019 Africa GP tutorial

  - [Part 1](https://youtu.be/U85XFCt3Lak), [Part 2](https://youtu.be/b635kuSqLww)

* [Machine Learning with Signal Processing (ICML 2020 Tutorial)](https://youtu.be/vTRD03_yReI)

* [Gaussian processes for fun and profit: Probabilistic machine learning in industry](https://youtu.be/uq8VxqeHPj8)

* [A Primer on Gaussian Processes for Regression Analysis | PyData NYC 2019](https://youtu.be/j7Ruu3Yu-70)

## 📦 Packages

List of packages dedicated to Gaussian Processes or with Gaussian Processes functionalities.

### Python

* [GPy](https://github.com/SheffieldML/GPy)

* [celerite](https://celerite.readthedocs.io/en/stable/)

* [GPyTorch](https://gpytorch.ai/)

* [GPflow](https://github.com/GPflow/GPflow)

* [BoTorch](https://botorch.org/)

* [scikit-learn GP module](http://scikit-learn.org/stable/modules/gaussian_process.html)

* [PyMC3](https://docs.pymc.io/Gaussian_Processes.html)

* [Pyro](https://pyro.ai/examples/gp.html)

* [GPJax](https://github.com/thomaspinder/GPJax)

* [Emukit](https://github.com/EmuKit/emukit)

* [Stheno](https://github.com/wesselb/stheno)

* [JAX-BO](https://github.com/PredictiveIntelligenceLab/JAX-BO)

### Julia

* [GaussianProcesses.jl](https://stor-i.github.io/GaussianProcesses.jl/latest/)

* [Stheno.jl](https://github.com/willtebbutt/Stheno.jl)

### Stan

 * [Stan User's Guide - Gaussian Processes chapter](https://mc-stan.org/docs/2_26/stan-users-guide/gaussian-processes-chapter.html)

### Octave / Matlab

* [GPstuff](https://research.cs.aalto.fi/pml/software/gpstuff/)

* [GPML toolbox](http://www.gaussianprocess.org/gpml/code/matlab/doc/)

## 📝 Publications

### Bayesian Optimization

* [A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning](https://arxiv.org/abs/1012.2599)

* [Taking the Human Out of the Loop: A Review of Bayesian Optimization](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)

### Causality

* [Causal Inference using Gaussian Processes with Structured Latent Confounders](http://proceedings.mlr.press/v119/witty20a/witty20a.pdf)

### Multiple-output Gaussian processes

* [Kernels for Vector-Valued Functions: a Review](https://arxiv.org/abs/1106.6251)

    

### Survival Analysis

* [Gaussian Processes for Survival Analysis](https://arxiv.org/abs/1611.00817)

### Time Series

* [Gaussian processes for time-series modelling](http://rsta.royalsocietypublishing.org/content/371/1984/20110550)

    

## 📌 Meetups

* [Gaussian Processes Cambridge](https://www.meetup.com/gaussian-processes-cambridge/)

    * [Resources](https://github.com/GaussianProcessesCambridge/meetup-resources)

## 🎉 Open to Suggestions!

See something that's missing from this list? PRs welcome!

If you're unsure if a paper or resource belongs in this list, feel free to open an issue and start a discussion. This repo is meant to be a community effort. So don't hesitate to voice an opinion.