Applied Bayesian for Analytics

By Professor Pulak Ghosh | Indian Institute of Management Bangalore(IIMB)

Learners enrolled: 1232

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.

Summary

Course Status :	Completed
Course Type :	Core
Duration :	6 weeks
Category :	Management Studies
Credit Points :	2
Level :	Postgraduate
Start Date :	31 Jan 2021
End Date :	30 Apr 2021
Enrollment Ends :	28 Feb 2021
Exam Date :

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

EXAMINATION:

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.

GRADING AND CERTIFICATE:

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.