Bayesian Learning

This is the home for the book Bayesian Learning, which is still work in progress. The book is currently used for the course Bayesian Learning at Stockholm University.

A pdf of the book will always be available, even after the book gets published.

The Notebooks tab contains Quarto/Jupyter/Pluto notebooks for the chapters in the book.

The Code tab contains code for some algorithms used in the book.

The Interactive tab contains interactive Observable widgets.

Contents

1. The Bayesics
2. Single-parameter models
3. Multi-parameter models
4. Priors
5. Regression
6. Prediction and Decision making
7. Normal posterior approximation
8. Classification
9. Posterior simulation
10. Variational inference
11. Regularization
12. Model comparison
13. Variable selection
14. Gaussian processes
15. Interaction models
16. Mixture models
17. Dynamic models and sequential inference
    Appendix: Some Mathematical results

Thanks to all who found typos and error in the book.