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Basic Details:

  • Course Code: EM 525
  • Credits: 3
  • Pre-requisites: EM212, EM213
  • Compulsory/Optional: Optional

Aim :

To introduce basic concepts of Bayesian analysis, including how to conduct posterior and predictive inference; learn how to use common Bayesian models in applications; learn common ways of prior elicitation; utilize R for Bayesian computation, visualization and analysis of real-world data.

Intended Learning Outcomes:

On successful completion of the course, the students should be able to;

  • Demonstrate a solid understanding of the basic Bayesian approach to inference, based on expressing all uncertainty in terms of conditional probability distributions.
  • Interpret Bayesian models in applied problems, including hierarchical model specification and computational techniques.

Couse Content:

Introduction to Bayesian Statistics.

Concepts and methods
of Bayesian inference, Bayesian hypothesis testing and model comparison, inference from binomial and multinomial data.

Normal distribution, Poisson distribution, exponential distribution and hypothesis testing.

Normal and multinomial distribution.

Markov chains, Monte Carlo and Markov Chain Monte Carlo (MCMC).

Hierarchical models, linear models, variable selection for linear models, hierarchical linear nonlinear models mixed models, generalized linear models and mixture models.

Time Allocation (Hours):

Lectures
0
Assignments
0

Recommended Texts:

  • J. Gill, “Bayesian Methods, A Social and Behavioral Sciences Approach”, 2nd edition,(2008), Chapman & Hall.
  • J.B. Carlin Gelman, H.S. Stern and D.B. Rubin, “Bayesian Data Analysis”, 2nd edition (2004), Chapman & Hall.
  • J. K. Doing Kruschke, “Bayesian Data Analysis”, 2ndedition, (2015), A Tutorial with R, JAGSand Stan, Academic Press.

Assessment:

In - course:

Tutorials/Assignments
30%
Mid Semester Examination
20%

End-semester:

50%