How to do Bayesian statistical modelling using numpy and PyMC3
To get started, first identify whether you:
- Prefer to use the
condapackage manager (which ships with the Anaconda distribution of Python), or if you
- prefer to use
pipenv, which is a package authored by Kenneth Reitz for package management with
virtualenv, or if you
- 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
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
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
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.
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!
Please see individual notebooks for dataset attribution.
Further Reading & Resources
Further reading resources that are not specifically tied to any notebooks.