{"id":30698540,"url":"https://github.com/satrialoka/bayes-resources","last_synced_at":"2026-02-12T06:04:59.079Z","repository":{"id":216845462,"uuid":"231350531","full_name":"satrialoka/bayes-resources","owner":"satrialoka","description":"Plethora of knowledge (papers, books, videos etc) in my journey to learn bayesian methods in machine learning","archived":false,"fork":false,"pushed_at":"2021-10-12T18:00:54.000Z","size":136,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-01-13T07:38:08.186Z","etag":null,"topics":["bayesian-methods","beginners-guide","references"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/satrialoka.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-01-02T09:36:55.000Z","updated_at":"2024-01-13T07:38:10.648Z","dependencies_parsed_at":"2024-01-13T07:38:10.157Z","dependency_job_id":"1a51cc4e-35e1-468a-8bd6-d8bc4b3c9f52","html_url":"https://github.com/satrialoka/bayes-resources","commit_stats":null,"previous_names":["satrialoka/bayes-resources"],"tags_count":0,"template":null,"template_full_name":null,"purl":"pkg:github/satrialoka/bayes-resources","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satrialoka%2Fbayes-resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satrialoka%2Fbayes-resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satrialoka%2Fbayes-resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satrialoka%2Fbayes-resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/satrialoka","download_url":"https://codeload.github.com/satrialoka/bayes-resources/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satrialoka%2Fbayes-resources/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273272577,"owners_count":25075982,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-02T02:00:09.530Z","response_time":77,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-methods","beginners-guide","references"],"created_at":"2025-09-02T10:49:05.571Z","updated_at":"2026-02-12T06:04:59.038Z","avatar_url":"https://github.com/satrialoka.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Bayes Resources\nPlethora of knowledge (papers, books, videos etc) in my journey to learn bayesian methods in machine learning\n\n## Books\n+ [ Machine Learning A Probabilistic Perspective](https://doc.lagout.org/science/Artificial%20Intelligence/Machine%20learning/Machine%20Learning_%20A%20Probabilistic%20Perspective%20%5BMurphy%202012-08-24%5D.pdf) (A good start to learn probabilistic machine learning, the first two introductory chapters are pretty good for starter) - Kevin P. Murphy \n\n+ [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/) - Richard McElreath | [course](https://github.com/rmcelreath/statrethinking_winter2019) | [numpyro implementation](https://fehiepsi.github.io/rethinking-numpyro/) | (seems good read later)\n\n+ [Surrogates](https://bobby.gramacy.com/surrogates/surrogates.pdf) - Robert B. Gramacy \n\n+ [Bayesian Optimization Book](https://bayesoptbook.com/) - Roman Garnett\n\n\n\n## Tutorials and Course Notes\n+ [Course Notes for Bayesian Models for Machine Learning](http://www.columbia.edu/~jwp2128/Teaching/BML_lecture_notes.pdf) (A must read note, the formula derivations are nice) - Columbia University Fall 2016\n\n+ [Variational Inference: A Review for Statisticians](https://arxiv.org/pdf/1601.00670.pdf) - Blei et al\n\n+ [An Introduction to Variational Methodsfor Graphical Models](https://people.eecs.berkeley.edu/~jordan/papers/variational-intro.pdf) - Jordan et al\n\n+ [Advanced methods of variational inference - Deep Bayes 2018](https://www.youtube.com/watch?v=mCBnid-1slI) - Max Welling\n\n+ [Matrix Cook Book](https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf) (many usefull facts on linear algebra and beyond)\n\n+ [Deep Probabilistic Modelling with with Gaussian Processes](http://inverseprobability.com/talks/notes/deep-probabilistic-modelling-with-gaussian-processes.html) - Neil D. Lawrence\n\n+ [Bayesian workflow](https://dpsimpson.github.io/pages/talks/Bayesian_Workflow.pdf) - Gelman et,al.\n+ [Neil Lawrence's Talks](http://inverseprobability.com/talks/) - Neil D. Lawrence\n\n## Bayesian Deep Learning\n\n+ [Bayesian Learning for Neural Networks](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.446.9306\u0026rep=rep1\u0026type=pdf) - Neal, Radford M.\n\n### Monte Carlo Dropout Related\n+ [Uncertainty in Deep Learning (PhD Thesis)](http://mlg.eng.cam.ac.uk/yarin/blog_2248.html) - Yarin Gal\n\n+ [Dropout as a Bayesian Approximation:Representing Model Uncertainty in Deep Learning](https://arxiv.org/pdf/1506.02142.pdf) - Yarin Gal\n\nnote that this method is little controversial following [Ian Osband](https://iosband.github.io/) note on NIPS 2016 workshop. \nmore details on [r/machinelearning](https://www.reddit.com/r/MachineLearning/comments/7bm4b2/d_what_is_the_current_state_of_dropout_as/) \n\n### Other Methods \n\n## Gaussian Process and Bayesian Optimization\n\n### Gaussian Process\n+ [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/) - Carl Edward Rasmussen. short intro : [link](https://www.cs.ubc.ca/~hutter/EARG.shtml/earg/papers05/rasmussen_gps_in_ml.pdf). video lecture : [link](http://videolectures.net/mlss03_rasmussen_gp/)\n\n+ [Sparse GP using pseudo Input](http://www.gatsby.ucl.ac.uk/~snelson/SPGP_up.pdf) - Snelson \u0026 Ghahramani\n\n+ [Scalable Gaussian process inference using variational methods](http://mlg.eng.cam.ac.uk/matthews/thesis.pdf) - Matthews Thesis. (good resources to learn variational GP)\n\n+ [A Practical Guide to Gaussian Processes](https://drafts.distill.pub/gp/) - Deisenroth, Luo, Van der Wilk\n\n+ [A Visual Exploration of Gaussian Processes](https://distill.pub/2019/visual-exploration-gaussian-processes/) - Görtler et al\n\n\n### Bayesian Optimization\n+ [A Tutorial on Bayesian OptimizationPeter](https://arxiv.org/pdf/1807.02811.pdf) - I. Frazier\n\n+ [Taking the Human Out of the Loop:A Review of Bayesian Optimization](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf) Nando Defreitas . more comprehensive than above\n\n## Bandit Algorithm\n\n+ [The Multi-Armed Bandit Problem and Its Solutions](https://lilianweng.github.io/lil-log/2018/01/23/the-multi-armed-bandit-problem-and-its-solutions.html) -Lilian Weng\n\n## Online Courses\n\n+ [Bayesian Method for Machine Learning - National Research University Higher School of Economics](https://www.coursera.org/learn/bayesian-methods-in-machine-learning) (Comprehensive, compact and intuitive course on bayesian method for machine learning, this is good start for getting basic intuition on bayesian methods)\n\n## Summer School\n\n+ [Deep|Bayes 2019](https://deepbayes.ru/) material and code: [github](https://github.com/bayesgroup/deepbayes-2019) video: [youtube](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW)\n\n## Blogs\n+ [betanalpha.github.io](https://betanalpha.github.io/writing/) - Michael Betancourt's blog\n+ [Notes on machine learning](https://peterroelants.github.io/) - Peter Roelants's blog (have good notes on GP, Multivariate Gaussian and Multi-Armed bandit)\n+ [Knowledge Gradient Visualized](https://tiao.io/post/an-illustrated-guide-to-the-knowledge-gradient-acquisition-function/) - Louis Tiao (Nice visualization to understand KG)\n\n## Notes etc\n+ [Optimal Transport and Wassertein Distance](http://www.stat.cmu.edu/~larry/=sml/Opt.pdf) - Larry Wasserman \n+ [Understanding ELBO](http://legacydirs.umiacs.umd.edu/~xyang35/files/understanding-variational-lower.pdf) - Xitong Yang\n+ [More on Multivariate Gaussians](http://cs229.stanford.edu/section/more_on_gaussians.pdf) - Chuong B. Do\n+ [Gaussian Densities](https://www.seas.upenn.edu/~sys502/extra_materials/MULTIVARIATE_NORMAL.pdf) - Tony E. Smith\n+ [Gumble Distribution](https://github.com/mrahtz/humble-gumbel/blob/master/gumbel.ipynb) - mrahtz \n+ [Expected Improvement Derivation](http://ash-aldujaili.github.io/blog/2018/02/01/ei/) - Al-Dujaili\n+ [Math for Machine Learning](http://users.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf) - Hal Daumé III\n### QnA\n+ [How to choose prior](https://stats.stackexchange.com/questions/78606/how-to-choose-prior-in-bayesian-parameter-estimation)\n+ [Standardizing data for GP Regression](https://stats.stackexchange.com/questions/178245/should-we-standardize-the-data-while-doing-gaussian-process-regression)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatrialoka%2Fbayes-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsatrialoka%2Fbayes-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatrialoka%2Fbayes-resources/lists"}