{"id":17191102,"url":"https://github.com/dfm/gp","last_synced_at":"2025-05-12T05:34:50.225Z","repository":{"id":14821127,"uuid":"17543756","full_name":"dfm/gp","owner":"dfm","description":"A tutorial about Gaussian process regression","archived":false,"fork":false,"pushed_at":"2020-09-03T15:07:39.000Z","size":17362,"stargazers_count":185,"open_issues_count":0,"forks_count":56,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-03-15T05:41:22.443Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dfm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-03-08T14:42:21.000Z","updated_at":"2024-11-13T13:58:45.000Z","dependencies_parsed_at":"2022-09-21T11:50:22.275Z","dependency_job_id":null,"html_url":"https://github.com/dfm/gp","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Fgp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Fgp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Fgp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Fgp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dfm","download_url":"https://codeload.github.com/dfm/gp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253684302,"owners_count":21947296,"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","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":[],"created_at":"2024-10-15T01:24:53.659Z","updated_at":"2025-05-12T05:34:50.193Z","avatar_url":"https://github.com/dfm.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"This repository contains an interactive IPython worksheet (`worksheet.ipynb`)\ndesigned to introduce you to Gaussian Process models. Only very minimal\nexperience with Python should be necessary to get something out of this.\n\nSome of this worksheet was originally prepared for a lab section at the Penn\nState Astrostats summer school in 2014 and it has been updated and adapted\nseveral times since then.\n\n**Remember**: the best reference for anything related to Gaussian Processes is\n[Rasmussen \u0026 Williams](http://www.gaussianprocess.org/gpml/).\n\n\nPrerequisites\n-------------\n\nYou'll need the standard scientific Python stack (numpy, scipy, and\nmatplotlib), a recent (3+) version of [IPython/Jupyter](http://jupyter.org/)\n(including the notebook), and [emcee](http://dfm.io/emcee) installed. If you\ndon't already have a working Python installation (and maybe even if you do), I\nrecommend using the [Anaconda distribution](http://continuum.io/downloads) and\nthen running `pip install emcee`.\n\n\nUsage\n-----\n\nAfter you have your Python environment set up, download the code from this\nrepository by running:\n\n```\ngit clone https://github.com/dfm/gp.git\n```\n\nor by [clicking here](https://github.com/dfm/gp/archive/master.zip).\n\nThen, navigate into the `gp` directory and run\n\n```\ncp worksheet.ipynb worksheet_in_progress.ipynb\njupyter notebook\n```\n\nThis might open a web browser with the correct URL, but if not, you can copy\nand paste the URL that it prints to the terminal into your browser.\nClick on `worksheet_in_progress.ipynb` to get started.\n\n\nLicense\n-------\n\nThis repository and the worksheet are copyright 2015-2017 Dan Foreman-Mackey and\nthey are made available under the terms of the MIT license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdfm%2Fgp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdfm%2Fgp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdfm%2Fgp/lists"}