{"id":13699443,"url":"https://github.com/dfm/emcee","last_synced_at":"2025-05-14T04:07:42.445Z","repository":{"id":1803323,"uuid":"2727330","full_name":"dfm/emcee","owner":"dfm","description":"The Python ensemble sampling toolkit for affine-invariant MCMC","archived":false,"fork":false,"pushed_at":"2025-03-16T16:58:56.000Z","size":34472,"stargazers_count":1509,"open_issues_count":59,"forks_count":432,"subscribers_count":84,"default_branch":"main","last_synced_at":"2025-05-03T05:03:36.691Z","etag":null,"topics":["mcmc","mcmc-sampler","probabilistic-data-analysis","python"],"latest_commit_sha":null,"homepage":"https://emcee.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"brennerm/PyTricks","license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dfm.png","metadata":{"files":{"readme":"README.rst","changelog":"HISTORY.rst","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":"AUTHORS.rst","dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2011-11-07T16:17:08.000Z","updated_at":"2025-04-30T18:21:32.000Z","dependencies_parsed_at":"2023-07-08T04:18:06.102Z","dependency_job_id":"2cbe457c-ba5d-4e84-897f-c5bd0c115ed0","html_url":"https://github.com/dfm/emcee","commit_stats":{"total_commits":796,"total_committers":75,"mean_commits":"10.613333333333333","dds":0.6268844221105527,"last_synced_commit":"4c3bfd2012316c6c62437c0a1eb5b57d93911c41"},"previous_names":[],"tags_count":23,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Femcee","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Femcee/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Femcee/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfm%2Femcee/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dfm","download_url":"https://codeload.github.com/dfm/emcee/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254059512,"owners_count":22007769,"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":["mcmc","mcmc-sampler","probabilistic-data-analysis","python"],"created_at":"2024-08-02T20:00:33.385Z","updated_at":"2025-05-14T04:07:42.410Z","avatar_url":"https://github.com/dfm.png","language":"Python","readme":"emcee\n=====\n\n**The Python ensemble sampling toolkit for affine-invariant MCMC**\n\n.. image:: https://img.shields.io/badge/GitHub-dfm%2Femcee-blue.svg?style=flat\n    :target: https://github.com/dfm/emcee\n.. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg\n    :target: https://github.com/dfm/emcee/actions?query=workflow%3ATests\n.. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat\n    :target: https://github.com/dfm/emcee/blob/main/LICENSE\n.. image:: http://img.shields.io/badge/arXiv-1202.3665-orange.svg?style=flat\n    :target: https://arxiv.org/abs/1202.3665\n.. image:: https://coveralls.io/repos/github/dfm/emcee/badge.svg?branch=main\u0026style=flat\u0026v=2\n    :target: https://coveralls.io/github/dfm/emcee?branch=main\n.. image:: https://readthedocs.org/projects/emcee/badge/?version=latest\n    :target: http://emcee.readthedocs.io/en/latest/?badge=latest\n\n\nemcee is a stable, well tested Python implementation of the affine-invariant\nensemble sampler for Markov chain Monte Carlo (MCMC)\nproposed by\n`Goodman \u0026 Weare (2010) \u003chttp://cims.nyu.edu/~weare/papers/d13.pdf\u003e`_.\nThe code is open source and has\nalready been used in several published projects in the Astrophysics\nliterature.\n\nDocumentation\n-------------\n\nRead the docs at `emcee.readthedocs.io \u003chttp://emcee.readthedocs.io/\u003e`_.\n\nAttribution\n-----------\n\nPlease cite `Foreman-Mackey, Hogg, Lang \u0026 Goodman (2012)\n\u003chttps://arxiv.org/abs/1202.3665\u003e`_ if you find this code useful in your\nresearch. The BibTeX entry for the paper is::\n\n    @article{emcee,\n       author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.},\n        title = {emcee: The MCMC Hammer},\n      journal = {PASP},\n         year = 2013,\n       volume = 125,\n        pages = {306-312},\n       eprint = {1202.3665},\n          doi = {10.1086/670067}\n    }\n\nLicense\n-------\n\nCopyright 2010-2021 Dan Foreman-Mackey and contributors.\n\nemcee is free software made available under the MIT License. For details see\nthe LICENSE file.\n","funding_links":[],"categories":["Probabilistic Methods","\u003cspan id=\"head30\"\u003e3.4. Bayesian Inference\u003c/span\u003e","Linear Algebra / Statistics Toolkit","Python","其他_机器学习与深度学习"],"sub_categories":["Others","\u003cspan id=\"head31\"\u003e3.4.1. 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