Material for course on Bayesian Computation
This tutorial assumes that you have some form of Anaconda Python (with Python version 3.11) setup and installed on your system. If you do not, please download and install this on your system before proceeding with the setup. We recommend using the Miniforge distribution of Anaconda, which is a lightweight version of Anaconda that is easier to work with.
The next step is to clone or download the tutorial materials in this repository. If you are familiar with Git, run the clone command:
git clone https://github.com/fonnesbeck/bayes_course_june_2024.git
otherwise you can download a zip file of its contents, and unzip it on your computer.
The repository for this tutorial contains a file called environment.yml that includes a list of all the packages used for the tutorial. If you run:
mamba env create
from the main tutorial directory (if you installed Anaconda instead of Miniforge, use conda instead of mamba), it will create the environment for you and install all of the packages listed. This environment can be enabled using:
mamba activate bayes_course
or
conda activate bayes_course
Then, you can start JupyterLab to access the materials:
jupyter lab
The binder link above should also provide a working environment.
For those who like to work in VS Code, you can also run Jupyter notebooks from within VS Code. To do this, you will need to install the Jupyter extension. Once this is installed, you can open the notebooks in the notebooks subdirectory and run them interactively.
In advance of the course, we would like attendees to complete a short homework notebook that will ensure everyone has the requisite baseline knowledge. You can find this Jupyter notebook in the /notebooks subdirectory (under Section0-Pre_Work.ipynb). There is no need to hand this in to anyone, but please reach out if you have difficulty with any of the problems (or with setting up your computing environment) by creating an issue in this repository, or by emailing.
- 8:00 to 9:30 First session
- 9:30 to 9:45 Break ☕
- 9:45 to 11:15 Second session
- 11:15 to 11:30 Break ☕
- 11:30 to 13:00 Third session
The course comprises ten modules of videoconference lectures, along with short associated hands-on projects to reinforce materials covered during lectures. The sections cover core materials related to Bayesian computation using PyMC, and include:
A Primer on Bayesian Inference
- Conditional probability and Bayes' formula
- The anatomy of a Bayesian model
- Probability density functions, inverse CDF sampling
- Bayesian comuptation and approximations
Introduction to Bayesian Models and PyMC
- The PyMC API
- My first PyMC model
- PyTensor
Markov chain Monte Carlo
- Rejection sampling
- MCMC basics
- Metropolis-Hastings samplers
- Gibbs samplers
- Problems with first-generation MCMC methods
- Using gradient information to improve MCMC
- Hamiltonian Monte Carlo
- The NUTS algorithm
PyMC Model Building
- Model building in PyMC
- Partial pooling
- Building hierarchical models
- Parameterizations
Hierarchical Models
- Parital pooling
- Random effects
- Prediction
Model Checking
- Convergence diagnostics
- Goodness-of-fit checks
- Model comparison
The Bayesian Workflow
- Prior predictive checks
- Iterating models
- Posterior predictive checks
- Generative modeling
- Using the model
Non-parametric Bayes
- Spline models
- Gaussian processes