This repository contains Python code (PyMC3) for a selection of models and figures from the book 'Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan', Second Edition, by John Kruschke (2015). The datasets used in this repository have been retrieved from the book's website. Note that, in its current form, this repository is not a 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!
<IMG src='https://9b8e0032-a-62cb3a1a-s-sites.googlegroups.com/site/doingbayesiandataanalysis/what-s-new-in-2nd-ed/CoverDBDA2E-FrontOnly-600wide.png' , height=30%, width=30%>
Chapter 9 - Hierarchical Models
Chapter 10 - Model Comparison and Hierarchical Modelling
Chapter 12 - Bayesian Approaches to Testing a Point ("Null") Hypothesis
Chapter 16 - Metric-Predicted Variable on One or Two Groups
Chapter 17 - Metric-Predicted Variable with One Metric Predictor
Chapter 18 - Metric Predicted Variable with Multiple Metric Predictors
Chapter 19 - Metric Predicted Variable with One Nominal Predictor
Chapter 20 - Metric Predicted Variable with Multiple Nominal Predictor
Extra: Bayesian Linear Regression example (Bishop, 2006)
Libraries used:
- pymc3
- pandas
- numpy
- scipy
- matplotlib
- seaborn
#####References: Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer Science+Business Media, New York. https://www.microsoft.com/en-us/research/people/cmbishop/
Kruschke, J.K., (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition, Academic Press / Elsevier. https://sites.google.com/site/doingbayesiandataanalysis/
#####Note:
The following repository 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.
https://github.com/aloctavodia/Doing_bayesian_data_analysis