Bayesian Learning

This online textbook follows the Bayesian Learning lecture sequence and turns slide material into fuller textbook chapters, with expanded explanations, worked derivations, and study checks.

Reading Path

Start with Introduction to Bayesian Inference, the first chapter covering likelihood, Bayes’ theorem, and Bernoulli models.

Chapter Tree

• Foundations
  • Introduction to Bayesian Inference
  • Normal and Poisson Models
  • Multiparameter Models
• Inference and Decisions
  • Prediction and Decision Theory
  • Regression and Regularization
  • Classification and Large-Sample Approximations
• Computational Methods
  • Monte Carlo and Gibbs Sampling
  • MCMC and Metropolis—Hastings
  • Hamiltonian Monte Carlo and Stan
• Model Comparison and Evaluation
  • Model Evaluation
  • Bayesian Model Comparison
  • Variable Selection and Model Averaging

Editorial Approach

• Follow the course outline and keep source lecture slides traceable.
• Expand slide bullets into definitions, explanations, derivations, and study checks.
• Add glossary entries as new technical terms appear.
• Reference: Bayesian Data Analysis, Third Edition (Gelman et al.).

Source

Lectures by Bertil Wegmann, Department of Computer and Information Science, Linköping University. Textbook: Bayesian Data Analysis, Third Edition by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (Chapters 1—11).