Statistical Rethinking 2023 in Python

Python/PyMC 5 implementations of Richard McElreath's outstanding lecture series Statistical Rethinking 2023.

Setup

All notebooks were developed on a Mac laptop with M1 Pro ARM64 chipset. The python environment is managed using miniconda. If you are on a similar hardware setup, you can initialize the environment via the standard conda workflow:

$ conda env create -f statistical-rethinking-2023.yml
$ conda activate statistical-rethinking-2023

Otherwise, the Python version and standard packages that are imported in the notebooks via the init_notebook.py script are as follows:

Watermark:
----------
Python implementation: CPython
Python version       : 3.10.12
IPython version      : 8.15.0

Compiler    : Clang 15.0.7 
OS          : Darwin
Release     : 22.2.0
Machine     : arm64
Processor   : arm
CPU cores   : 10
Architecture: 64bit

statsmodels: 0.14.0
numpy      : 1.24.4
arviz      : 0.16.1
scipy      : 1.11.2
pymc       : 5.8.0
pandas     : 2.1.0
xarray     : 2023.8.0
matplotlib : 3.7.2

Name		Name	Last commit message	Last commit date
Latest commit History 34 Commits
data		data
images		images
.gitignore		.gitignore
LICENSE		LICENSE
Lecture 02 - The Garden of Forking Data.ipynb		Lecture 02 - The Garden of Forking Data.ipynb
Lecture 03 - Geocentric Models.ipynb		Lecture 03 - Geocentric Models.ipynb
Lecture 04 - Categories & Curves.ipynb		Lecture 04 - Categories & Curves.ipynb
Lecture 05 - Elemental Confounds.ipynb		Lecture 05 - Elemental Confounds.ipynb
Lecture 06 - Good & Bad Controls.ipynb		Lecture 06 - Good & Bad Controls.ipynb
Lecture 07 - Fitting Over & Under.ipynb		Lecture 07 - Fitting Over & Under.ipynb
Lecture 08 - Markov Chain Monte Carlo.ipynb		Lecture 08 - Markov Chain Monte Carlo.ipynb
Lecture 09 - Modeling Events.ipynb		Lecture 09 - Modeling Events.ipynb
Lecture 10 - Counts & Hidden Confounds.ipynb		Lecture 10 - Counts & Hidden Confounds.ipynb
Lecture 11 - Ordered Categories.ipynb		Lecture 11 - Ordered Categories.ipynb
Lecture 12 - Multilevel Models.ipynb		Lecture 12 - Multilevel Models.ipynb
Lecture 13 - Multilevel Adventures.ipynb		Lecture 13 - Multilevel Adventures.ipynb
Lecture 14 - Correlated Features.ipynb		Lecture 14 - Correlated Features.ipynb
Lecture 15 - Social Networks.ipynb		Lecture 15 - Social Networks.ipynb
Lecture 16 - Gaussian Processes.ipynb		Lecture 16 - Gaussian Processes.ipynb
Lecture 17 - Measurement and Misclassification.ipynb		Lecture 17 - Measurement and Misclassification.ipynb
Lecture 18 - Missing Data.ipynb		Lecture 18 - Missing Data.ipynb
Lecture 19 - Generalized Linear Madness.ipynb		Lecture 19 - Generalized Linear Madness.ipynb
Lecture 20 - Horoscopes.ipynb		Lecture 20 - Horoscopes.ipynb
README.md		README.md
TODO.md		TODO.md
init_notebook.py		init_notebook.py
statistical-rethinking-2023.mplstyle		statistical-rethinking-2023.mplstyle
statistical-rethinking-2023.yml		statistical-rethinking-2023.yml
utils.py		utils.py

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dustinstansbury/statistical-rethinking-2023

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Statistical Rethinking 2023 in Python

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