Advanced Bayesian Methods: Theory and Applications in R

Outline

Day 1

  1. Principles of Bayesian Inference
  2. Markov Chain Monte Carlo Simulations
  3. Monitoring Mixing and Convergence
  4. Posterior Summaries
  5. Penalized Spline Smoothing
  6. A Generic Basis Function Framework

Day 2

  1. Spatial Smoothing
  2. GAMLSS
  3. Model Checking
  4. Families
  5. Big Data and Variable Selection
  6. Machine Learning

Course Materials

Data and course materials are available at ABM.