🗓️ Week 02 - Bayesian linear regression

Author

Affiliation

Dr. Simone Rossi

EURECOM

In this second week, we cover Bayesian Linear Regression: applying Bayesian inference to linear models for regression. We will introduce likelihoods and priors, derive posterior updates for conjugate models, discuss predictive distributions, and address model selection and uncertainty with practical examples and exercises.

🖥️ Lecture Slides

• Bayesian Linear Regression

📝 Lecture Notes (PDF)

• Bayesian Linear Regression

📝 Exercises

• Bayesian linear regression exercises

📚 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