Lecture 1: Basics of Bayesian inference

Readings:

  • A First Course in Bayesian Statistical Methods by Peter D. Hoff.:
    • Section 1: Introduction and examples
    • Section 3.1: The binomial model
    • Section 3.4: Discussion and further references
  • Bayesian Data Analysis (Third Edition) by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin:
    • Section 1.1: The three steps of Bayesian data analysis
    • Section 1.2: General notation for statistical inference
    • Section 1.3: Bayesian inference
    • Section 1.9: Computation and software
    • Section 1.10: Bayesian inference in applied statistics
    • Section 2.1: Estimating a probability from binomial data
    • Section 2.2: Posterior as compromise between data and prior information
    • Section 2.4: Informative prior distributions

Optional