Applied Bayesian for Analytics (edX)

This course is divided into two parts i.e. Theoretical and Empirical part of Bayesian Analytics. First three weeks cover the Theoretical part which includes how to form a prior, how to calculate a posterior and several other aspects. Rest of the weeks will cover the empirical part which explains how to compute Bayesian modelling. Completion of this course will provide you with an understanding of the Bayesian approach, the primary difference between Bayesian and Frequentist approaches and experience in data analyses.

What you'll learn

• 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.

Syllabus

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