This repo contains the notebooks and python scripts for the afternoon labs of the Learning from Small Data course.
Hierarchical model example:https://github.com/fonnesbeck/multilevel_modeling
Notebook 1: Intro to PyMC3, linear and non-linear regression, first models and basic exercises.
Notebook 2: Hierarchical models. Theory in https://docs.pymc.io/notebooks/multilevel_modeling.html, based on Gelman and Hill 2006. Data from BIAS project http://www.bias-project.org.uk/WB2011Man/CourseData/BHMData.zip.
Notebook 3: more complex exercises from the book http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
Bayesian Methods for Hackers
http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
Adapted companion repo of BDA3 https://github.com/pymc-devs/resources/tree/master/BDA3 Bayesian Data Analysis is the 'bible' of Bayesian thinking. Although those notebooks are not fully detailed, they are a great way to follow what is done in the book and the state of the art of the field.
Hierarchical Models : https://github.com/pymc-devs/resources/blob/master/BDA3/chap_05.ipynb
Repo of Statistical Rethinking https://github.com/pymc-devs/resources/tree/master/Rethinking Again, these notebooks are not the best documented, but they make really easy following the examples in the book.
Repo for Doing Bayesian Data Analysis https://github.com/JWarmenhoven/DBDA-python Examples from the Krushke book adapted to PyMC3.