├── .gitignore ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 Raúl Peralta Lozada 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Gaussian Processes 2 | 3 | A list of resources for understanding Gaussian Processes. Inspired by [Awesome Normalizing Flows](https://github.com/janosh/awesome-normalizing-flows) list. 4 | 5 |
6 | 7 | ## Table of Contents 8 | 9 | 1. [📘 Books](#-books) 10 | 2. [🌐 Blog Posts](#-blog-posts) 11 | 3. [📺 Videos](#-videos) 12 | 4. [📦 Packages](#-packages) 13 | 5. [📝 Publications](#-publications) 14 | 6. [📌 Meetups](#-meetups) 15 | 7. [🎉 Open to Suggestions!](#-open-to-suggestions) 16 | 17 |
18 | 19 | ## 📘 Books 20 | 21 | * [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/) 22 | * [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) 23 | * [Bayesian Data Analysis 3rd Edition - Chapter 21](http://www.stat.columbia.edu/~gelman/book/) 24 | * [Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences](https://bookdown.org/rbg/surrogates/) 25 | * [Machine Learning: a Probabilistic Perspective - Chapter 15](https://www.cs.ubc.ca/~murphyk/MLbook/) 26 | * [Applied Stochastic Differential Equations](https://users.aalto.fi/~ssarkka/pub/sde_book.pdf) 27 | 28 | ### Thesis 29 | 30 | * [Automatic Model Construction with Gaussian Processes](https://www.cs.toronto.edu/~duvenaud/thesis.pdf) by David K. Duvenaud 31 | * [Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian processes](http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf) by Andrew G. Wilson 32 | 33 | ### Other resources 34 | 35 | * [Gaussian Process Model Zoo](https://jejjohnson.github.io/gp_model_zoo/) by J. Emmanuel Johnson 36 | 37 | ## 🌐 Blog Posts 38 | 39 | ### Introductory 40 | 41 | * [A Visual Exploration of Gaussian Processes](https://distill.pub/2019/visual-exploration-gaussian-processes/) 42 | * [Gaussian process introductory tutorial in Python¶](http://adamian.github.io/talks/Damianou_GP_tutorial.html) 43 | * [Gaussian Processes, not quite for dummies](https://thegradient.pub/gaussian-process-not-quite-for-dummies/) 44 | * [Robust Gaussian Process Modeling](https://betanalpha.github.io/assets/case_studies/gaussian_processes.html) 45 | * [The Kernel Cookbook](http://www.cs.toronto.edu/~duvenaud/cookbook/index.html) 46 | * [Interactive Gaussian Process Visualization](http://www.infinitecuriosity.org/vizgp/) 47 | 48 | ### Applications 49 | 50 | * [Gaussian process demonstration with Stan](https://avehtari.github.io/casestudies/Motorcycle/motorcycle_gpcourse.html) by Aki Vehtari 51 | * [Gaussian Process Classification Model in various PPLs](https://luiarthur.github.io/TuringBnpBenchmarks/gpclassify) 52 | * [Exploring Bayesian Optimization](https://distill.pub/2020/bayesian-optimization/) 53 | * [Random effects in Gaussian Processes](https://martiningram.github.io/gp-random-effects/) 54 | 55 | ## 📺 Videos 56 | * [Gaussian Process Summer Schools](http://gpss.cc/) 57 | * [Gaussian Process Basics](http://videolectures.net/gpip06_mackay_gpb/) by David MacKay 58 | * [Gaussian Processes](http://videolectures.net/mlss09uk_rasmussen_gp/) by Carl E. Rasmussen 59 | * [Introduction to Gaussian processes](https://youtu.be/4vGiHC35j9s) by Nando de Freitas 60 | * [ Open Data Science Initiative](https://www.youtube.com/channel/UCUjuEqUQbTrJ11f8nkWltQQ) channel 61 | * [MLSS 2013 Tübingen](http://mlss.tuebingen.mpg.de/2013/index.html) GP Tutorial 62 | - [Part 1](https://youtu.be/50Vgw11qn0o), [Part 2](https://youtu.be/TR0LCVslIIM), [Part 3](https://youtu.be/KRLW5abMV6s) 63 | * [MLSS 2015 Tübingen](http://mlss.tuebingen.mpg.de/2015/index.html) GP Tutorial 64 | - [Part 1](https://youtu.be/S9RbSCpy_pg), [Part 2](https://youtu.be/MxeQIKGEXb8), [Part 3](https://youtu.be/Ead4TivIOmU) 65 | * MLSS 2019 Africa GP tutorial 66 | - [Part 1](https://youtu.be/U85XFCt3Lak), [Part 2](https://youtu.be/b635kuSqLww) 67 | * [Machine Learning with Signal Processing (ICML 2020 Tutorial)](https://youtu.be/vTRD03_yReI) 68 | * [Gaussian processes for fun and profit: Probabilistic machine learning in industry](https://youtu.be/uq8VxqeHPj8) 69 | * [A Primer on Gaussian Processes for Regression Analysis | PyData NYC 2019](https://youtu.be/j7Ruu3Yu-70) 70 | 71 | ## 📦 Packages 72 | 73 | List of packages dedicated to Gaussian Processes or with Gaussian Processes functionalities. 74 | 75 | ### Python 76 | 77 | * [GPy](https://github.com/SheffieldML/GPy) 78 | * [celerite](https://celerite.readthedocs.io/en/stable/) 79 | * [GPyTorch](https://gpytorch.ai/) 80 | * [GPflow](https://github.com/GPflow/GPflow) 81 | * [BoTorch](https://botorch.org/) 82 | * [scikit-learn GP module](http://scikit-learn.org/stable/modules/gaussian_process.html) 83 | * [PyMC3](https://docs.pymc.io/Gaussian_Processes.html) 84 | * [Pyro](https://pyro.ai/examples/gp.html) 85 | * [GPJax](https://github.com/thomaspinder/GPJax) 86 | * [Emukit](https://github.com/EmuKit/emukit) 87 | * [Stheno](https://github.com/wesselb/stheno) 88 | * [JAX-BO](https://github.com/PredictiveIntelligenceLab/JAX-BO) 89 | 90 | ### Julia 91 | 92 | * [GaussianProcesses.jl](https://stor-i.github.io/GaussianProcesses.jl/latest/) 93 | * [Stheno.jl](https://github.com/willtebbutt/Stheno.jl) 94 | 95 | ### Stan 96 | * [Stan User's Guide - Gaussian Processes chapter](https://mc-stan.org/docs/2_26/stan-users-guide/gaussian-processes-chapter.html) 97 | 98 | ### Octave / Matlab 99 | * [GPstuff](https://research.cs.aalto.fi/pml/software/gpstuff/) 100 | * [GPML toolbox](http://www.gaussianprocess.org/gpml/code/matlab/doc/) 101 | 102 | 103 | ## 📝 Publications 104 | 105 | ### Bayesian Optimization 106 | * [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) 107 | * [Taking the Human Out of the Loop: A Review of Bayesian Optimization](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf) 108 | 109 | ### Causality 110 | * [Causal Inference using Gaussian Processes with Structured Latent Confounders](http://proceedings.mlr.press/v119/witty20a/witty20a.pdf) 111 | 112 | ### Multiple-output Gaussian processes 113 | * [Kernels for Vector-Valued Functions: a Review](https://arxiv.org/abs/1106.6251) 114 | 115 | ### Survival Analysis 116 | * [Gaussian Processes for Survival Analysis](https://arxiv.org/abs/1611.00817) 117 | 118 | ### Time Series 119 | * [Gaussian processes for time-series modelling](http://rsta.royalsocietypublishing.org/content/371/1984/20110550) 120 | 121 | 122 | ## 📌 Meetups 123 | 124 | * [Gaussian Processes Cambridge](https://www.meetup.com/gaussian-processes-cambridge/) 125 | * [Resources](https://github.com/GaussianProcessesCambridge/meetup-resources) 126 | 127 | ## 🎉 Open to Suggestions! 128 | See something that's missing from this list? PRs welcome! 129 | 130 | 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. 131 | --------------------------------------------------------------------------------