Statistical Rethinking is an incredible good introductory book to Bayesian Statistics, its follows a Jaynesian and practical approach with very good examples and clear explanations.
In this repository we are porting the examples in the book to PyMC3. We are trying to keep the examples as close as possible to those in the book, while at the same time trying to express them in the most Pythonic and PyMC3onic way we can.
- Chapter 1, this chapter does not include code!
- Chapter 2-12, fully ported :-)
- Chapter 13, partially ported :-|
- Chapter 14, needs to be ported :-(
All contributions are welcome!
Feel free to send PR to fix errors, improve the code or made comments that could help the user of this repository and readers of the book. Check the issues list to see which chapter are already assigned, or where we need help.
If you want to contribute with an entire chapter or a big section, please check the issues tracker to see which chapters are not assigned yet and need contributors. You can also chat with us Gitter
To install the dependencies to run these notebooks, you can use Anaconda. Once you have installed Anaconda, run:
conda env create -f environment.yml
to install all the dependencies into an isolated environment. You can switch to this environment by running:
source activate pymc
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License.
