🗓️ Week 04 - Introduction to Approximate Inference and MCMC

Author

Affiliation

Dr. Simone Rossi

EURECOM

In this week, we analyze inference in probabilistic models, starting with an introduction to approximate inference and sampling-based methods. We will cover basic Monte Carlo methods (rejection sampling), Markov Chain Monte Carlo (MCMC) for sampling from complex distributions, and gradient-based methods to improve sampling efficiency with a focus on Hamiltonian Monte Carlo (HMC).

🖥️ Lecture Slides

• Introduction to Approximate Inference
• Monte Carlo
• Markov Chain Monte Carlo

📑 Lecture Notes (PDF)

• Introduction to Approximate Inference
• Monte Carlo
• Markov Chain Monte Carlo

📚 Recommended Reading

• Murphy (2023): Chapter 7.4 (Approximate Inference)
• Murphy (2023): Chapter 11.1, 11.2, 11.3, 11.4 (Monte Carlo methods)
• Murphy (2023): Chapter 12.1, 12.2, 12.5, 12.6 (Markov Chain Monte Carlo)

📙 References

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