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How to do Bayesian statistical modelling using numpy and PyMC3.

If you're looking for the material for a specific conference tutorial, navigate to the notebooks directory and look for a subdirectory for the conference you're interested. For example,notebooks/ODSC-East-2020-04-14 contains the material forHugo's ODSC East tutorial on April 14, 2020.

To get started, first identify whether you:

• Would like to run the tutorial material on servers hosted elsewhere, to avoid installation,
• Prefer to use theconda package manager (which ships with the Anaconda distribution of Python),
• Prefer to usepipenv, which is a package authored by Kenneth Reitz for package management withpip andvirtualenv, or
• Only want to view the website version of the notebooks.

To do this, click on theBinder badge above. This will spin up the necessary computational environment for you so you can write and execute Python code from the comfort of your browser. It is a free service. Due to this, the resources are not guaranteed, though they usually work well. If you want as close to a guarantee as possible, follow the instructions below to set up your computational environment locally (that is, on your own computer).

In your terminal, usegit 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.

If you do not already have theAnaconda distribution of Python 3,go get it(note: you can also set up your project environment w/out Anaconda usingpip 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 theconda package managertoinstall all the necessary packagesfrom the providedenvironment.yml file.

conda env create -f binder/environment.yml

Toactivate the environment, use theconda activate command.

conda activate bayesian-modelling-tutorial

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

source activate bayesian-modelling-tutorial

Toupdate the environment based on theenvironment.yml specification file, use theconda update command.

conda env update -f binder/environment.yml

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

pip install networkx scipy ...

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.

1. You will have to install a new IPython kernelspec if you created a new conda environment withbinder/environment.yml.

   python -m ipykernel install --user --name bayesian-modelling-tutorial --display-name "Python (bayesian-modelling-tutorial)"

You can change the--display-name to anything you want, though if you leave it out, the kernel's display name will default to the value passed to the--name flag.

2. In the terminal, executejupyter notebook.

Navigate to the notebooks directoryand open the notebook01-Student-Probability_a_simulated_introduction.ipynb.

In the terminal, executejupyter lab.

Navigate to the notebooks directoryand open the notebook01-Student-Probability_a_simulated_introduction.ipynb.

Now, if you're using Jupyter lab, for Notebook 2, you'll need to get ipywidgets working.The documentation ishere.

In short, you'll need node installed & you'll need to run the following in your terminal:

jupyter labextension install @jupyter-widgets/jupyterlab-manager

Launch Binder using the button at the top of this README.md. Voila!

If you're interested in only viewing the static HTML versions of the notebooks,the links are provided below:

Part 1: Bayesian Data Science by Simulation

• Introduction to Probability
• Parameter Estimation and Hypothesis Testing

Part 2: Bayesian Data Science by Probabilistic Programming

• Two Group Comparisons: Drug effect on IQ
• Multi-Group Comparisons: Multiple ways of sterilizing phones
• Two Group Comparisons: Darwin's Finches
• Hierarchical Modelling: Baseball
• Hierarchical Modelling: Darwin's Finches
• Bayesian Curve Regression: Identifying Radioactive Element

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 leave feedback for ushere!We'll use this information to help improve the teaching and delivery of the material.

Please see individual notebooks for dataset attribution.

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

• Visualization in Bayesian workflow
• PyMC3 examples gallery
• Bayesian Analysis Recipes
• Communicating uncertainty about facts, numbers and science
• Bernoulli's Fallacy by Aubrey Clayton