Bayesian Learning
Requirements:
- Minimal background in mathematics and statistic
- Analysis and calculus (integral, derivatives, study of functions, … )
- Basic statistics concepts
- Basic expertise with Python and Jupyter Notebook
Course Material all the material is available at this page.
| Lesson | Support Slides | Notebook | Additional Material | References |
|---|---|---|---|---|
| Day 1 | Introduction | Basic Probability Theory | / | / |
| Day 2 | / | Inference in Bayesian models: theory and practice | / | [BDA]:Ch1, [McE]:Ch3 |
| Day 3 | / | Laplace approximation | / | tools.py, Data (from [McE]) |
| Day 4 | / | Bayesian linear regression | / | [McE]:Ch4 |
| Day 5 | / | MCMC | / | [McE]:Ch8, [Bet2018], [Stan] |
| Day 6 | Advanced MCMC (a) | / | MCMC with Pyro (a) MCMC with Pyro (b) | [McE]:Ch8, Ch10, [Pyro] |
| Day 7 | / | A primer on variational inference with Pyro | / | [Pyro] |
| Day 8 | / | Practical Variational Inference with Pyro | / | [Pyro] |
References:
- [BDA]. Bayesian Data Analysis. A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, D.B. Rubin; Chapman and Hall/CRC, 2014, 3rd Edition.
- [McE] Statistical Rethinking. R. McElreath; Chapman and Hall/CRC, 2016, 3rd Edition
- [Bet2018] A Conceptual Introduction to Hamiltonian Monte Carlo. M. Betancourt; ArXiv
- [Stan] Stan Documentation. Stan Development Team
- [Pyro] Pyro website