STCS 44123

>< Type/ Status : Core for students offering the special degree in Statistics and Computer Science.
>< Title : Bayesian Inference and Decision Theory.
>< Pre – requisites : STCS 11015
>< Objectives : At the end of this unit the student will be able to solve real life problems arising in the fields of Bayesian Inference and Decision Theory.
>< Course Content :

Bayesian Inference- Bayes Theorem (the discrete and the continuous case), Likelihood, Posterior distributions, Posterior inference, Prior distribution, Bayesian approaches to typical statistical questions, Bayesian inference for some univariate probability models, Bayes methods when models contain many parameters. Game Theory and Decision Theory- Basic Ideas,
Mathematical framework A comparison of Game theory and decision theory, Minimax and Bayes Decision rules, decision function, risk function, Utility and subjective probability, Randomization, Optimal decision rules, Statistics and Decision Theory, risk altitudes and utility theory.

>< Methodology : A combination of lectures and tutorials
>< Scheme of Evaluation : End of course examination
>< Recommended Reading :
1.O’Hagan Anthony, Bayesian inference (Kendall’s Advanced theory of Statistics – Vol:2B), (1994, 1st Edition).
2.Gelman Andrew, Carlin John .B.,.Hall, S and, Rubin, D., Bayesian Data Analysis, (1995, 1st Edition), Chapman & Hall.
3.Ferguson, T.S , Mathematical Statistics- A decision theory approach ', (1967, 1st Edition), Academic Press.
4.Ledermann Walter, Loyd Emlyn, Handbook of applicable Mathematics, (1994, 1st Edition), Edward Arnold.

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