Material for course on Bayesian Computation
This tutorial assumes that you have Anaconda (Python 3.11) setup and installed on your system. If you do not, please download and install Anaconda on your system before proceeding with the setup.
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_2023.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:
conda env create
from the main tutorial directory, it will create the environment for you and install all of the packages listed. This environment can be enabled using:
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.
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.
The course comprises six two-hour 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:
- 8:00 to 9:30 First session
- 9:30 to 9:45 Break
- 9:45 to 11:15 Second session
- 11:15 to 11:45 Lunch
- 11:45 to 12:45 Third session
- 12:45 to 13:00 Break
- 13:00 to 14:00 Fourth session
Introduction to Bayesian Models and PyMC
- The anatomy of a Bayesian model
- Probability density functions, inverse CDF sampling
- Bayesian comuptation and approximations
- The PyMC API
- 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
- NUTS
Hierarchical Models
- Parital pooling
- Random effects
- Prediction
PyMC Model Building and Model Checking
- Model building in PyMC
- Partial pooling
- Building hierarchical models
- Parameterizations
- Convergence diagnostics
- Goodness-of-fit checks
- Model comparison
Non-parametric Bayes
- Dirichlet processes
- Spline models
- Gaussian processes
The Bayesian Workflow
- Prior predictive checks
- Iterating models
- Posterior predictive checks
- Generative modeling
- Using the model