🗓️ Week 03 - Lab on Bayesian Linear Regression

Author

Affiliation

Dr. Simone Rossi

EURECOM

This week features a lab session on Bayesian Linear Regression. We will work through practical examples to solidify understanding of concepts from the previous week: 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. Click the badge below to open the notebook in Colab:

Open In Colab

📚 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)

📙 References

Murphy, K. P. (2022). Probabilistic machine learning: An introduction. MIT Press. http://probml.github.io/book1

Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT Press. http://probml.github.io/book2