Bayesian Data Analysis by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin is a comprehensive, standard, and wonderful textbook on Bayesian Methods. There currently exist code for examples in the book in R, Python, and Matlab, all using the Stan language.
This repository is a work in progress, organizing work on porting examples and exercises to Python and PyMC3. Please open a pull request on the README to indicate interest in a chapter or section!
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Probability and Inference
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Single-parameter models
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Introduction to multiparameter models
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Asymptotics and connections to non-Bayesian approaches
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Hierarchical models
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Model checking
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Evaluating, comparing, and expanding models
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Modeling accounting for data collection
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Decision analysis
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Introduction to Bayesian computation
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Basics of Markov chain simulation
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Computationally efficient Markov chain simulation
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Modal and distributional approximations
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Introduction to regression models
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Hierarchical linear models
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Generalized linear models
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Models for robust inference
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Models for missing data
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Parametric nonlinear models
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Basis function models
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Gaussian process models
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Finite mixture models
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Dirichlet process models
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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.
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 bda3-pymc3
Bayesian Data Analysis with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License.
