{"id":17192710,"url":"https://github.com/adrn/princetonstatsseminar","last_synced_at":"2026-05-15T22:04:25.374Z","repository":{"id":141959278,"uuid":"80380683","full_name":"adrn/PrincetonStatsSeminar","owner":"adrn","description":"Supplementary material for my lecture on 6 Feb.","archived":false,"fork":false,"pushed_at":"2020-06-21T15:11:33.000Z","size":4249,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-09-09T00:18:40.790Z","etag":null,"topics":["adrn-teaching","bayesian-inference","probability","python","statistics"],"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/adrn.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-01-29T23:36:06.000Z","updated_at":"2017-09-19T00:21:48.000Z","dependencies_parsed_at":"2024-05-31T08:45:15.300Z","dependency_job_id":null,"html_url":"https://github.com/adrn/PrincetonStatsSeminar","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/adrn/PrincetonStatsSeminar","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adrn%2FPrincetonStatsSeminar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adrn%2FPrincetonStatsSeminar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adrn%2FPrincetonStatsSeminar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adrn%2FPrincetonStatsSeminar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/adrn","download_url":"https://codeload.github.com/adrn/PrincetonStatsSeminar/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adrn%2FPrincetonStatsSeminar/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279005548,"owners_count":26083918,"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":"2025-10-10T02:00:06.843Z","response_time":62,"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":["adrn-teaching","bayesian-inference","probability","python","statistics"],"created_at":"2024-10-15T01:29:57.038Z","updated_at":"2025-10-10T23:16:09.189Z","avatar_url":"https://github.com/adrn.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Princeton Stats Workshop (6 Feb)\n\nSupplementary material for my lecture on model fitting.\n\n# Dependencies\n\nI recommend using [anaconda](https://anaconda.org/) as your Python distribution and package\nmanager. (If you don't already use it, you can download it and install a new version of Python\npretty harmlessly. It will install everything to a new directory so it won't mess with your\nexisting installation). One of the great advantages of Anaconda is the ability to create [conda\nenvironments](https://conda.io/docs/using/envs.html) that let you maintain different, independent\nPython installations (e.g., you can have Python 2 and 3 installed in parallel) and easily switch\nbetween them. I recommend creating a new environment for this material with all of the packages\nwe'll need, just to sandbox everything and make sure we're all using the same versions. If you\nclone or download this repo, you can use the\n[environment.yml](https://github.com/adrn/PrincetonStatsSeminar/blob/master/environment.yml) file\nto automatically create the environment for you. After cloning / downloading, change directories\ninto the project root and do:\n\n    conda env create\n\nOnce that's done installing everything, you need to activate the environment to use the version of\nPython with all of the packages it just installed. Do that with:\n\n    source activate statsseminar\n\nTo deactivate the environment (if you want to go back to your root environment / default context)\nyou just have to do:\n\n    source deactivate\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadrn%2Fprincetonstatsseminar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadrn%2Fprincetonstatsseminar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadrn%2Fprincetonstatsseminar/lists"}