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  • 2:34 PM, Saturday, 08 Aug 2020


Course Postgraduate
Semester Sem. II
Subject Code MA625
Subject Title Statistical Models and Analysis

Syllabus

An overview of basic probability theory and theory of estimation; Bayesian statistics; maximum a posteriori (MAP) estimation; conjugate priors; Exponential family; posterior asymptotics; linear
statistical models; multiple linear regression: inference technique for the general linear model, generalised linear models: inference procedures, special case of generalised linear models leading
to logistic regression and log linear models; introduction to non-linear modelling; sampling methods: basic sampling algorithms, rejection sampling, adaptive rejection sampling, sampling and the EM
algorithm, Markhov chain, Monte Carlo, Gibbs sampling, slice sampling.

Text Books

Same as Reference

References

1. Dobson, A. J. and Barnett, A. G., An Introduction to Generalised Linear Models, 3rd ed., Chapman and Hall/CRC (2008).
2. Krzanowski, W. J., An Introduction to Statistical Modeling, Wiley (2010).
3. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer (2002).
4. Bishop, C. M., Pattern Recognition and Machine Learning, Springer (2006).