• Understand the necessary Bayesian concepts from practical point of view for better decision making.
• Learn Bayesian approach to estimate likely event outcomes, or probabilities using datasets.
• Gain “hands on” experience in creating and estimating Bayesian models using R and OPENBUGS.

Week 01: What is Bayesian Statistics and How it is different than Classical Statistics

• Foundations of Bayesian Inference
• Bayes theorem
• Advantages of Bayesian models
• Why Bayesian approach is so important in Analytics
• Major densities and their applications

Week 02: Bayesian analysis of Simple Models

• Likelihood theory and Estimation
• Parametrizations and priors
• Learning from binary models
• Learning from Normal Distribution

Week 03: Monte Carlo Methods

• Basics of Monte carol integration
• Basics of Markov chain Monte Carlo
• Gibs Sampling

Week 04: Computational Bayes

• Examples of Bayesian Analytics
• Introduction to R and OPENBUGS for Bayesian analysis

Week 05: Bayesian Linear Models

• Context for Bayesian Regression Models
• Normal Linear regression
• Logistic regression

Week 06: Bayesian Hierarchical Models

• Introduction to Multilevel models
• Exchangeability
• Computation in Hierarchical Models