This course is designed to provide a foundation for Bayesian statistical research.  The course balances theory, computation and application.  The main topics are:

  • Methods for selecting prior distributions
  • Frequentist properties of Bayesian methods
  • Advanced computing for large datasets
  • Hierarchical modeling
  • High-dimensional analysis
  • Bayesian machine learning

For more information, view the course syllabus.