🗓️ Week 03 - Lab on Bayesian Linear Regression

Author

Affiliation

Dr. Simone Rossi

EURECOM

In this week, we will have a lab session focused on Bayesian Linear Regression. We will work through practical examples to solidify understanding of the concepts introduced in the previous week, like likelihood, prior distribution, posterior updates, model selection and model uncertainty.

🧪 Lab

Labs are available on GitHub. The lab for this week is Bayesian Linear Regression.

Check the README file for instructions on how to run the lab.

Google Colab is the suggested way to edit and execute the labs. Simply click on the icon to open the corresponding notebook in Colab.

For this week, you can

📚 Recommended Reading

• Murphy (2022): Chapter 11.1, 11.2, 11.3 (Linear regression)
• Murphy (2022): Chapter 11.7 (Bayesian linear regression)
• Murphy (2023): Chapter 13.2 (Bayesian linear regression)
• Murphy (2023): Chapter 3.2.1 (Coin toss exercise)

📙 References

Murphy, Kevin P. 2022. Probabilistic Machine Learning: An Introduction. MIT Press. http://probml.github.io/book1.

———. 2023. Probabilistic Machine Learning: Advanced Topics. MIT Press. http://probml.github.io/book2.