Contents

Chapter 1: Bayes’ Theorem & Probability Foundations	3
Chapter 2: The Great Debate: Frequentist vs. Bayesian Statistics	6
Chapter 3: Conjugate Priors & Posterior Distributions	7
Chapter 4: Bayesian Estimators and Credible Intervals	9
Chapter 5: Loss function and decision theory	11
Chapter 6: Bayesian Hypothesis Testing & Model Comparison	13
Chapter 7: Markov Chain Monte Carlo (MCMC) – Part I	15
Chapter 8: MCMC – Part II (Advanced Topics)	17
Chapter 9: Variational Inference	19
Chapter 10: Bayesian Linear and Logistic Regression	20
Chapter 11: Hierarchical (Multilevel) Bayesian Models	23
Chapter 12: Model Diagnostics and Posterior Predictive Checks	25
Chapter 13: Bayesian Model Averaging	26
Chapter 14: Prior Elicitation and Sensitivity Analysis	28
Chapter 15: Applications – Econometrics, Finance, and Machine Learning	29