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project aims to reproduce analyses done in the book\nBayesian Data Analysis (Gelman et al, 3rd Edition) in Python\nwithout relying on black-box Bayesian inference libraries,\nso that I can familiarize myself with conducting Bayesian inference in Python.\nHopefully this is a useful resource for other people as well.\n\nHere is the list of notebooks I wrote:\n\n* [Hierarchical Bayesian Inference of Binomial Probabilities (on rat tumor data, Chapter 5.3)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/ch5_3_rat_tumor.ipynb)\n* [Hierarchical Bayesian Inference of Group Normal Means (on SAT coaching data and beta-blocker data, Chapter 5.4, 5.5, 5.6, 6.5, 7.3)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/hierarchical_normal.ipynb)\n* [Basic Monte-Carlo Markov Chain (MCMC) Sampling (on bivariate normal distribution, Chapter 11.1, 11.3, 11.4)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/basic_mcmc.ipynb)\n* [MCMC sampling on Hierarchical Normal Model with Unknown Standard Deviation (Chapter 11.6)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/mcmc_hierarchical_normal.ipynb)\n* [Logistic regression with grid sampling (Chapter 3.7), mode-based approximation (Chapter 4.1), and Expectation Propagation (Chapter 13.8)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/simple_logistic_regression.ipynb)\n* [Hierarchical Bayesian Linear Regression (Chapter 15.2)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/presidential.ipynb)","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbikestra%2Fbdapy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbikestra%2Fbdapy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbikestra%2Fbdapy/lists"}