bayesian-stats-modelling-tutorial

How to do Bayesian statistical modelling using numpy and PyMC3

getting started

To get started, first identify whether you:

1. Prefer to use the conda package manager (which ships with the Anaconda distribution of Python), or if you
2. prefer to use pipenv, which is a package authored by Kenneth Reitz for package management with pip and virtualenv, or if you
3. Do not want to mess around with dev-ops.

1. Clone the repository locally

In your terminal, use git to clone the repository locally.

git clone https://github.com/ericmjl/bayesian-stats-modelling-tutorial

Alternatively, you can download the zip file of the repository at the top of the main page of the repository. If you prefer not to use git or don't have experience with it, this a good option.

2. Download Anaconda (if you haven't already)

If you do not already have the Anaconda distribution of Python 3, go get it (note: you can also set up your project environment w/out Anaconda using pip to install the required packages; however Anaconda is great for Data Science and we encourage you to use it).

3. Set up your environment

3a. conda users

If this is the first time you're setting up your compute environment, use the conda package manager to install all the necessary packages from the provided environment.yml file.

conda env create -f environment.yml

To activate the environment, use the conda activate command.

conda activate bayesian-modelling-tutorial

If you get an error activating the environment, use the older source activate command.

source activate bayesian-modelling-tutorial

To update the environment based on the environment.yml specification file, use the conda update command.

conda env update -f environment.yml

3b. pip users

Please install all of the packages listed in the environment.yml file manually. An example command would be:

pip install networkx scipy ...

3c. don't want to mess with dev-ops

If you don't want to mess around with dev-ops, click the following badge to get a Binder session on which you can compute and write code.

Acknowledgements

Development of this type of material is almost always a result of years of discussions between members of a community. We'd like to thank the community and to mention several people who have played pivotal roles in our understanding the the material: Michael Betancourt, Justin Bois, Allen Downey, Chris Fonnesbeck, Jake VanderPlas. Also, Andrew Gelman rocks!

data credits

Please see individual notebooks for dataset attribution.

Further Reading & Resources

Further reading resources that are not specifically tied to any notebooks.

• Visualization in Bayesian workflow
• PyMC3 examples gallery
• Bayesian Analysis Recipes