Good statisticians are able to explain their choices, justify their numbers, evaluate their own models, and share their results (in a reproducible fashion)! This tutorial demonstrates how to do all the above, using ArviZ.

To get started, first identify whether you:

1. Prefer to use the conda package manager (which ships with the Anaconda distribution of Python),
2. Prefer to use pip
3. Prefer to use docker
4. Do not want to mess around with dev-ops, or
5. Only want to view the website version of the notebooks.

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

git clone [email protected]:arviz-devs/bayesian-model-evaluation.git 

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.

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).

If this is the first time you're setting up your compute environment, use the conda to create an environment.

conda create -n bayes-eval

To activate the environment, use the conda activate command.

conda activate bayes-eval

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

source activate bayes-eval

Then follow the instructions for pip users.

Please install all of the packages listed in the requirements.txt file.

pip install -r requirements.txt

An image can be built from the root directory of the repository using the command. This will build an image on your computer with all dependencies and environment

./scripts/container.sh --build

Once an image is built a container can be started with the command

./scripts/container.sh --notebook

In your terminal a URL for the notebook server will be displayed. Copy and paste that into a browser. With that you'll have Jupyter in a container! If you're using docker you can skip step 4.

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.

In the terminal, navigate to this directory and execute jupyter lab.

Navigate to the notebooks then 1_BayesianWorkflow and open notebook 1_Ins_BayesRefresher.ipynb.

If you're interested in only viewing the static HTML versions of the notebooks you can view them on github

We would like to thank the whole Bayes community for being open with learnings and material. For this tutorial in particular we'd like to thank Ari Hartikainen, Osvaldo Martin, and Eric Ma for providing feedback and content.

Please leave feedback for us here! We'll use this information to help improve the teaching and delivery of the material. Issues and pull requests are also encouraged if you would like!