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Decision Theory and Bayesian Inference I

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PURPOSE To equip the students with skills to build statistical models for non-trivial problems when data is sparse and expert opinion needs to be incorporated and to use the key features of a Bayesi ... an problem and algorithms for Bayesian Analysis. OBJECTIVES By the end of this course the student should be able to; (i) Explain and apply Bayes’ rule and other decision rules. (ii) Explain the likelihood principle and derive a posterior distribution from a prior distribution. (iii) Perform classification, hypothesis testing and estimation. (iv) Explain the subjectivism point of view. (v) Apply Bayesian inference and analysis for the normal and binomial distributions. (vi) Apply the basic concepts in decision analysis. DESCRIPTION Bayes’ rule, loss and risk functions, and minimax rules. Likelihood principle, prior and posterior distribution. Classification and hypothesis testing. Estimation in decision framework. Subjectivism point of view. Bayesian inference for normal distribution. Bayesian analysis for binomial data. Basic concepts in decision analysis: influence diagrams, decision trees, and utility theory. PRE-REQUISITES: STA 2300 Theory of Estimation, STA 2304 Decision theory [Show More]

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