This repository contains Python/PyMC code designed as an introduction for those familiar with introductory statistical concepts, likely from a null hypothesis significance testing (NHST) perspective. The code began life as a python implementation of models/figures from the book Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition, by John Kruschke (2015), but has meandered a bit since then. Given the age of that book and the ever-advancing development of PyMC (and Bayesian modeling more generally), attempting to faithfully reproduce the book's models has seemed less and less useful. It is hoped that any discrepancies between the models found here and those originally specified in the book represent progress.
The datasets used in this repository have been retrieved from the book's website. Note that, this repository is not a standalone tutorial and will be far more helpful with guidance (e.g., Kruschke's book). Questions, suggestions for improvement, and/or help with further development of the notebooks are always welcome!
Chapter 5 - Bayes' Rule
Chapter 6 - Inferring a Binomial Probability via Exact Mathematical Analysis
Chapter 7 - Markov Chain Monte Carlo
Chapter 9 - Hierarchical Models
Chapter 10 - Model Comparison and Hierarchical Modelling
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
Chapter 21 - Dichotomous Predicted Variable
Chapter 22 - Nominal Predicted Variable
Chapter 23 - Ordinal Predicted Variable
Chapter 24 - Count Predicted Variable
Other notebooks:
Posterior Predictive Checking
Bayes' Factors
Libraries used/required:
- pymc
- arviz
- pandas
- numpy
- scipy
- matplotlib
- seaborn
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/
Kruschke, J.K. & Liddell, T.M. (2017), The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective, Psychonomic Bulletin & Review, http://dx.doi.org/10.3758/s13423-016-1221-4
Kruschke, J.K. & Liddell, T.M. (2017), Bayesian data analysis for newcomers, Psychonomic Bulletin & Review, http://dx.doi.org/10.3758/s13423-017-1272-1
Salvatier J, Wiecki TV, Fonnesbeck C. (2016), Probabilistic programming in Python using PyMC3, PeerJ Computer Science 2:e55, https://doi.org/10.7717/peerj-cs.55
PyMC3 - http://pymc-devs.github.io/pymc3/
The 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.