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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.