Bayesian Statistics

Overview

Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among other Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.

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.

Prof. Pulak Ghosh

Course Instructor
Decision Science
Indian Institute of Management Bangalore (IIMB)

Full Profile
Course Details

6 weeks

3 - 4 hrs per week

Course Syllabus

Week 1 - 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 2 - Bayesian analysis of Simple Models

 

  • Likelihood theory and Estimation
  • Parametrizations and priors
  • Learning from binary models
  • Learning from Normal Distribution
Week 3 - Monte Carlo Methods

 

  • Basics of Monte carol integration
  • Basics of Markov chain Monte Carlo
  • Gibs Sampling
Week 4 - Computational Bayes

 

  • Examples of Bayesian Analytics
  • Introduction to R and OPENBUGS for Bayesian analysis
Week 5 - Bayesian Linear Models

 

  • Context for Bayesian Regression Models
  • Normal Linear regression
  • Logistic regression
Week 6 - Bayesian Hierarchical Models

 

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

By the end of this MOOC you will be able to:

  • 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.

Business school students, working professionals and anyone who is interested in learning Bayesian statistics with the basic knowledge of mathematics.