Bayesian Learning - the books

Bayesian Learning

This book is suitable for advanced undergraduate and master’s level courses, with some of the more advanced chapters more suited for a PhD course, or a specialized master’s level course.

The book is currently used for the master’s course Bayesian Learning and the PhD course Advanced Bayesian Learning.

Bayesian Learning - the prequel

This book contains all basic mathematics and statistics needed to read the Bayesian Learning book.

The prequel book is used on the intermediate statistics course Statistical Theory and Modelling as part of the Master’s Program in Data Science, Statistics and Decision Analysis.

Contents

  1. The Bayesics
  2. Single-parameter Models
  3. Multi-parameter Models
  4. Priors
  5. Linear Regression
  6. Prediction and Decision Making
  7. Normal Posterior Approximation
  8. Classification and Generalized Regression
  9. Gibbs Sampling
  10. Markov Chain Monte Carlo Simulation
  11. Variational Inference
  12. Regularization
  13. Mixture Models and Bayesian Nonparametrics
  14. Model Comparison and Variable Selection
  15. Gaussian Processes
  16. Interaction Models
  17. Dynamic Models and Sequential Inference
    Appendix: Some Mathematical Results

Contents

  1. Mathematics
  2. Probability
  3. Discrete random variables
  4. Continuous random variables
  5. Convergence and central theorems
  6. Transformation of random variables
  7. Joint distributions
  8. Likelihood inference
  9. Regression
  10. Time series

PDF versions of the books will always be available, even after they get published.

The exercises tab contains solutions to many of the problems in the book, including the computer-based exercises.

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.

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