https://github.com/bikestra/bdapy
Reproducing plots of Bayesian Data Analysis (Gelman et al, 3rd Edition) in Python
https://github.com/bikestra/bdapy
bayesian bayesian-data-analysis gelman mcmc python statistics
Last synced: about 2 months ago
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Reproducing plots of Bayesian Data Analysis (Gelman et al, 3rd Edition) in Python
- Host: GitHub
- URL: https://github.com/bikestra/bdapy
- Owner: bikestra
- License: apache-2.0
- Created: 2015-01-02T07:20:11.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2015-03-21T06:20:07.000Z (almost 11 years ago)
- Last Synced: 2023-08-06T14:10:49.225Z (over 2 years ago)
- Topics: bayesian, bayesian-data-analysis, gelman, mcmc, python, statistics
- Language: Python
- Size: 1.07 MB
- Stars: 46
- Watchers: 8
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
bdapy
=====
This project aims to reproduce analyses done in the book
Bayesian Data Analysis (Gelman et al, 3rd Edition) in Python
without relying on black-box Bayesian inference libraries,
so that I can familiarize myself with conducting Bayesian inference in Python.
Hopefully this is a useful resource for other people as well.
Here is the list of notebooks I wrote:
* [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)
* [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)
* [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)
* [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)
* [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)
* [Hierarchical Bayesian Linear Regression (Chapter 15.2)](http://nbviewer.ipython.org/github/bikestra/bdapy/blob/master/presidential.ipynb)