{"id":20198238,"url":"https://github.com/beekill95/numpyro-doing-bayesian","last_synced_at":"2025-04-10T10:50:56.404Z","repository":{"id":41100078,"uuid":"492237849","full_name":"beekill95/numpyro-doing-bayesian","owner":"beekill95","description":"My implementation of John K. 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Kruschke's\n[Doing Bayesian Data Analysis 2nd edition](https://sites.google.com/site/doingbayesiandataanalysis/what-s-new-in-2nd-ed)\nusing Python and Numpyro.\nThis implementation is not comprehensive,\nI'll just focus on the generalized linear model only,\nwhich is from chapter 16 onward.\nSuggestions for improvement are welcome!\n\n## Chapters\n\n* [Chapter 9: Hierarchical Models](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_09)\n* [Chapter 10: Model Comparison and Hierarchical Modeling](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_10)\n* [Chapter 12: Bayesian Approaches to Testing a Point (\"Null\") Hypothesis](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_12)\n* [Chapter 16: Metric-Predicted Variable on One or Two Groups](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_16)\n* [Chapter 17: Metric Predicted Variable with one Metric Predictor](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_17)\n* [Chapter 18: Metric Predicted Variable with Multiple Metric Predictors](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_18)\n* [Chapter 19: Metric Predicted Variable with One Nominal Predictor](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_19)\n* (WIP) [Chapter 20: Metric Predicted Variable with Multiple Nominal Predictors](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_20)\n* [Chapter 21: Dichotomous Predicted Variable](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_21)\n    * [Exercise 21.3: Heterogeneous Concentration Parameters](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_21_exercise_21_3)\n* [Chapter 22: Nominal Predicted Variable](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_22)\n* [Chapter 23: Ordinal Predicted Variable](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_23)\n    * (WIP) [Exercise 23.2: Handle Outliers](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_23_exercise_23_2)\n* [Chapter 24: Count Predicted Variable](https://www.nguyenmbquan.space/numpyro-doing-bayesian/chapter_24)\n\n## Dependencies\n\nThis project uses a combination of [Conda](https://docs.conda.io/en/latest/)\nand [Poetry](https://python-poetry.org/) for dependencies management.\nTo install the dependencies for this project, make sure that you have `conda` installed on your system.\n\nFirst, create a virtual environment managed by `conda`:\n\n```\nconda env create -f environment.yml\n```\n\nThe above command will create a virtual environment named `doing_bayes`\nand install `poetry` package manager into that environment.\n\nAfter that, activate the environment `conda activate doing_bayes`\nand use `poetry` to install the remaining dependencies:\n\n```\npoetry install\n```\n\n## Jupyter Notebook\n\nActivate the `doing_bayes` environment,\nand then start the `jupyter-lab` server:\n\n```\njupyter-lab --no-browser\n```\n\nThen, you can click on the link to open notebooks on your browsers.\n\nEach chapter's notebook are a normal python script thanks to [Jupytext](https://jupytext.readthedocs.io/en/latest/).\nTo generate a notebook for a chapter from the python script, you can follow this [instruction](https://jupytext.readthedocs.io/en/latest/paired-notebooks.html#how-to-open-scripts-with-either-the-text-or-notebook-view-in-jupyter).\n\n## Credits\n\nMy implementation refers to [JWarmenhoven's implementation](https://github.com/JWarmenhoven/DBDA-python) a lot,\nespecially those figures with data and posterior predictive distributions.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbeekill95%2Fnumpyro-doing-bayesian","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbeekill95%2Fnumpyro-doing-bayesian","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbeekill95%2Fnumpyro-doing-bayesian/lists"}