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Chapter_03_00_Bayesian_CLT.ipynb
Chapter_03_00_Bayesian_CLT.ipynb
 
 
Chapter_03_01_Gibbs_sampling_one_sample_t-test.ipynb
Chapter_03_01_Gibbs_sampling_one_sample_t-test.ipynb
 
 
Chapter_03_02_Gibbs_sampling_two_sample_t-test.ipynb
Chapter_03_02_Gibbs_sampling_two_sample_t-test.ipynb
 
 
Chapter_03_03_Gibbs_sampling_for_simple_linear_regression.ipynb
Chapter_03_03_Gibbs_sampling_for_simple_linear_regression.ipynb
 
 
Chapter_03_04_Gibbs_sampling_for_concussions_data.ipynb
Chapter_03_04_Gibbs_sampling_for_concussions_data.ipynb
 
 
Chapter_03_05_Step_by_step_illustration_of_Metropolis_sampling.ipynb
Chapter_03_05_Step_by_step_illustration_of_Metropolis_sampling.ipynb
 
 
Chapter_03_06_Metropolis_sampling_for_the_concussions_data.ipynb
Chapter_03_06_Metropolis_sampling_for_the_concussions_data.ipynb
 
 
Chapter_03_07_Metropolis_sampling_for_the_concussions_data_with_adaptive_tuning.ipynb
Chapter_03_07_Metropolis_sampling_for_the_concussions_data_with_adaptive_tuning.ipynb
 
 
Chapter_03_08_Gibbs_Metropolis_sampling_for_simulated_data.ipynb
Chapter_03_08_Gibbs_Metropolis_sampling_for_simulated_data.ipynb
 
 
Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb
Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb
 
 
Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb
Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb
 
 
Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb
Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb
 
 
Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb
Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb
 
 
Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb
Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb
 
 
Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb
Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb
 
 
README.md
README.md
 
 
environment.yml
environment.yml
 
 

README.md

Bayesian Statistical Methods Python and PyMC

In this repository we port the book's original code to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a Pythonic and PyMConic way.

Display notebooks

Binder

Contributing

All contributions are welcome!

Feel free to send a PR to fix errors, improve the code, or make comments that could help users of this repository and readers of the book.

Installing the dependencies

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.

Activate the environment by running:

source activate bsm-pymc

Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License.