{"id":18907023,"url":"https://github.com/jwarmenhoven/dbda-python","last_synced_at":"2025-04-04T16:14:00.770Z","repository":{"id":45299332,"uuid":"63216177","full_name":"JWarmenhoven/DBDA-python","owner":"JWarmenhoven","description":"Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code ","archived":false,"fork":false,"pushed_at":"2021-08-13T09:18:07.000Z","size":77541,"stargazers_count":669,"open_issues_count":3,"forks_count":264,"subscribers_count":29,"default_branch":"master","last_synced_at":"2024-07-31T21:54:05.518Z","etag":null,"topics":["bayesian-data-analysis","bayesian-inference","hierarchical-models","kruschke","mcmc","probabilistic-programming","pymc3"],"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/JWarmenhoven.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":"2016-07-13T05:12:39.000Z","updated_at":"2024-06-28T08:51:05.000Z","dependencies_parsed_at":"2022-07-13T15:30:55.733Z","dependency_job_id":null,"html_url":"https://github.com/JWarmenhoven/DBDA-python","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/JWarmenhoven%2FDBDA-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JWarmenhoven%2FDBDA-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JWarmenhoven%2FDBDA-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JWarmenhoven%2FDBDA-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JWarmenhoven","download_url":"https://codeload.github.com/JWarmenhoven/DBDA-python/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247208142,"owners_count":20901570,"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":["bayesian-data-analysis","bayesian-inference","hierarchical-models","kruschke","mcmc","probabilistic-programming","pymc3"],"created_at":"2024-11-08T09:19:29.897Z","updated_at":"2025-04-04T16:14:00.750Z","avatar_url":"https://github.com/JWarmenhoven.png","language":"Jupyter Notebook","readme":"# Doing Bayesian Data Analysis - Python/PyMC3\nThis repository contains Python/\u003cA href=\"https://docs.pymc.io/\"\u003ePyMC3\u003c/A\u003e code for a selection of models and figures from the book \u003cA target=\"_blank\" href='https://sites.google.com/site/doingbayesiandataanalysis/'\u003e'Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan'\u003c/A\u003e, Second Edition, by John Kruschke (2015).\nThe datasets used in this repository have been retrieved from the book's website. Note that, in its current form, this repository is not a standalone tutorial and that you probably should have a copy of the book to follow along. Suggestions for improvement and help with unsolved issues are welcome!\u003cP\u003e\nNote that the code is in Jupyter Notebook format and requires modification to use with other datasets.\u003cP\u003e\nSome of the general concepts from the book are discussed in papers by Kruschke \u0026 Liddell. See references below.\n\u003c/P\u003e\n\n**2018-08-16:**  \nUpdating the notebooks with PyMC3 v3.5 and general code clean-up. Inserting plots of the PyMC models in plate notation (v3.5 feature). Fixing some deprecation warnings.\n \n\u003c/P\u003e\n\u003cIMG src='Notebooks/images/DBDA2.png' height=30% width=30%\u003e\u003cP\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%209.ipynb'\u003eChapter 9 - Hierarchical Models\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2010.ipynb'\u003eChapter 10 - Model Comparison and Hierarchical Modelling\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2012.ipynb'\u003eChapter 12 - Bayesian Approaches to Testing a Point (\"Null\") Hypothesis\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2016.ipynb'\u003eChapter 16 - Metric-Predicted Variable on One or Two Groups\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2017.ipynb'\u003eChapter 17 - Metric-Predicted Variable with One Metric Predictor\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2018.ipynb'\u003eChapter 18 - Metric Predicted Variable with Multiple Metric Predictors\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2019.ipynb'\u003eChapter 19 - Metric Predicted Variable with One Nominal Predictor\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2020.ipynb'\u003eChapter 20 - Metric Predicted Variable with Multiple Nominal Predictor\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2021.ipynb'\u003eChapter 21 - Dichotomous Predicted Variable\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2022.ipynb'\u003eChapter 22 - Nominal Predicted Variable\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2023.ipynb'\u003eChapter 23 - Ordinal Predicted Variable\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%2024.ipynb'\u003eChapter 24 - Count Predicted Variable\u003c/A\u003e\u003cP\u003e\nExtra:\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/Various-Machine-Learning-bits/blob/master/Bayesian%20Linear%20Regression.ipynb'\u003eBayesian Linear Regression example (Bishop, 2006)\u003c/A\u003e\u003cBR\u003e\n\u003cA href='http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Ordinal%20Model_Kruschke_Liddell.ipynb'\u003eExample on modelling Ordinal Data (Liddell \u0026 Kruschke, 2018)\u003c/A\u003e\n\u003cP\u003e\nLibraries used:\n\n - pymc3\n - theano\n - pandas\n - numpy\n - scipy\n - matplotlib\n - seaborn  \n\n#### References:\nBishop, C.M. (2006), \u003cI\u003ePattern Recognition and Machine Learning\u003c/I\u003e, Springer Science+Business Media, New York. https://www.microsoft.com/en-us/research/people/cmbishop/\u003cP\u003e\nKruschke, J.K. (2015), \u003cI\u003eDoing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan\u003c/I\u003e, Second Edition, Academic Press / Elsevier, https://sites.google.com/site/doingbayesiandataanalysis/\n\u003cP\u003e\nKruschke, J.K. \u0026 Liddell, T.M. (2017), \u003cI\u003eThe Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective\u003c/I\u003e, Psychonomic Bulletin \u0026 Review, http://dx.doi.org/10.3758/s13423-016-1221-4\n\u003cP\u003e\nKruschke, J.K. \u0026 Liddell, T.M. (2017), \u003cI\u003eBayesian data analysis for newcomers\u003c/I\u003e, Psychonomic Bulletin \u0026 Review, http://dx.doi.org/10.3758/s13423-017-1272-1\n\u003cP\u003e\nLiddell, T., \u0026 Kruschke, J. K. (2018, April 5). Analyzing ordinal data with metric models: What could possibly go wrong? Retrieved from http://osf.io/3tkz4 \n \u003cP\u003e\nSalvatier J, Wiecki TV, Fonnesbeck C. (2016), \u003cI\u003eProbabilistic programming in Python using PyMC3\u003c/I\u003e, PeerJ Computer Science 2:e55, https://doi.org/10.7717/peerj-cs.55 \u003cBR\u003e\nPyMC3 - http://pymc-devs.github.io/pymc3/\n\n#### Note:\nThe repository below contains python code for the first edition of the book. The code in that repository is a much more direct implementation of the R/JAGS code from the book than you will find here.\u003cBR\u003e\nhttps://github.com/aloctavodia/Doing_bayesian_data_analysis\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjwarmenhoven%2Fdbda-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjwarmenhoven%2Fdbda-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjwarmenhoven%2Fdbda-python/lists"}