Repository with notebooks (and solutions) for my Bayesian tutorial at the PyLadies Meetup Feb 11, 2020.
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 $(pwd)) jupyter lab # or jupyter notebook
To install pipenv, run
pip install pipenv
Then install the necessary packages, using
cd PyLadies-Bayesian-Tutorial pipenv install
To activate the environment and start the notebooks from it, run
pipenv shell python -m ipykernel install --user --name=$(basename $(pwd)) jupyter lab # or jupyter notebook
Then, inside jupyter, pick the according kernel for the notebooks.
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 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.