Home

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
• Markov Chain Monte Marlo
• Hierarchical modeling
• Model selection and evaluation
• Advanced modeling and machine learning
• Advanced computing for large datasets