Upon successful completion of this course students will be able to

• Compute Bayesian estimates for a wide variety of statistical models, using R software.
• Knowledge of fundamental sampling algorithms for performing posterior inference based on alternative prior distributions.
• Computer implementation using real and synthetic data.
• Applied problem solving using Bayesian statistical methodologies.