Applied Bayesian for Analytics

By Professor Pulak Ghosh   |   Indian Institute of Management Bangalore(IIMB)
Learners enrolled: 1638
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 others 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.

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.
Course Status : Completed
Course Type : Core
Duration : 6 weeks
Category :
  • Management Studies
Credit Points : 2
Level : Postgraduate
Start Date : 30 Jul 2021
End Date : 30 Oct 2021
Enrollment Ends : 15 Sep 2021
Exam Date : 14 Nov 2021 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

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

Instructor bio

Professor Pulak Ghosh

Indian Institute of Management Bangalore(IIMB)
Pulak Ghosh
Professor, Decision Sciences at Indian Institute of Management

Professor Ghosh's key specializations are in intersection of Big data, Machine learning, Artificial Intelligence and its use in Economics, Finance, Policy and Social Value Creation. He did serve in the editorial board of Journal of the American statistical Association, Journal of the Royal statistical Society and currently serves in the editorial board of Biometrics.

Based on his outstanding and innovative contribution to research, the International Indian Statistical Association awarded him with the ``Young Scientist Award” in 2011. Govt of India awarded him the prestigious CR Rao award in 2015 and Econometric Society awarded him the Mahalanobis Award in 2016. Prior to joining IIMB, he served as Associate Director, Novartis Pharmaceuticals, USA, Assistant Professor, Georgia State University, and Associate Professor at Emory University, USA.

Course certificate


Type of exam: Computer based exam 

You will have to appear at the allotted exam centre and produce your Hall ticket and Government Photo Identification Card for verification and take the exam in person. 
You can find the final allotted exam centre details in the hall ticket. The questions will be on the computer and the answers will have to be entered on the computer; type of questions may include multiple choice questions, fill in the blanks, essay type (subjective) type, etc. 


Weightage: 100% weightage of final exam. 

Passing Marks: You will be eligible for Certificate only if you score minimum 40% in Final Exam. 

If you score less than 40% in final exam, you will not receive the certificate. 

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