Doing Bayesian Data Analysis - Python/PyMC3

This repository contains Python/<A href="https://docs.pymc.io/">PyMC3</A> code for a selection of models and figures from the book <A target="_blank" href='https://sites.google.com/site/doingbayesiandataanalysis/'>'Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan'</A>, 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 standalone 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!<P> Note that the code is in Jupyter Notebook format and requires modification to use with other datasets.<P> Some of the general concepts from the book are discussed in papers by Kruschke & Liddell. See references below.

2018-08-16:
Updating the notebooks with PyMC3 v3.5 and general code clean-up. Inserting plots of the PyMC models in plate notation (v3.5 feature). Fixing some deprecation warnings.

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
Chapter 21 - Dichotomous Predicted Variable
Chapter 22 - Nominal Predicted Variable
Chapter 23 - Ordinal Predicted Variable
Chapter 24 - Count Predicted Variable

Extra:
Bayesian Linear Regression example (Bishop, 2006)
Example on modelling Ordinal Data (Liddell & Kruschke, 2018)

Libraries used:

• pymc3
• theano
• pandas
• numpy
• scipy
• matplotlib
• seaborn

References:

Bishop, C.M. (2006), <I>Pattern Recognition and Machine Learning</I>, Springer Science+Business Media, New York. https://www.microsoft.com/en-us/research/people/cmbishop/<P> Kruschke, J.K. (2015), <I>Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan</I>, 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

Liddell, T., & Kruschke, J. K. (2018, April 5). Analyzing ordinal data with metric models: What could possibly go wrong? Retrieved from http://osf.io/3tkz4

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/

Note:

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.<BR> https://github.com/aloctavodia/Doing_bayesian_data_analysis