PyLadies-Bayesian-Tutorial

Repository with notebooks (and solutions) for my Bayesian tutorial at the PyLadies Meetup Feb 11, 2020.

Setup

Download the code from Github

The recommended way to download the code is through git:

git clone https://github.com/corriebar/PyLadies-Bayesian-Tutorial.git

This will download all code and create the folder PyLadies-Bayesian-Tutorial in your current folder.

Install all packages

Using Conda (recommended)

To install the packages using conda, use the following command:

conda env create -f environment.yml

To activate the environment and start the notebook from it, run

conda activate PyLadies-Bayesian-Tutorial
ipython kernel install --user --name=$(basename $(pwd))
jupyter lab
# or jupyter notebook

Using Pipenv

To install pipenv, run

pip install pipenv

Then install the necessary packages, using

cd PyLadies-Bayesian-Tutorial
pipenv install

To activate the environment and start the notebooks from it, run

pipenv shell
python -m ipykernel install --user --name=$(basename $(pwd))
jupyter lab
# or jupyter notebook

Then, inside jupyter, pick the according kernel for the notebooks.

Using Pip

You can also install the packages from the requirements.txt file using pip:

pip install -r requirements.txt

Check that it works and extract the data

Open the notebook 1_Introduction.ipynb in the folder notebooks and try to run the first cell. If it can load all the packages and runs without problems then you should be good to go for the rest of the tutorial!

The tutorial

The tutorial consists of four notebooks:

• Introduction which contains some installation checks & extracts the data as well as short motivation why we'd want to use Bayesian methods. If you already know why to use Bayesian methods then this can easily be skipped (except for the installation cell).
• In Starting simple, we have a short look at our data and the start constructing a linear regression in PyMC3. We then learn how to understand your prior and experiment with different priors.
• In Did it converge, we then finally run our first model and check if everything went well. We'll also have a first look at the results.
• To go beyond linear, we then extend our linear model by adding some hierarchies.

The notebooks in the notebook folders contain small exercises and some missing code. If you prefer to just tag along with the tutorial or get lost at some point, the full notebooks can be found in solutions.