Dr Kostas Kalogeropoulos, COL 6.10

Optional on BSc Business Mathematics and Statistics, BSc Actuarial Science, BSc Accounting and Finance and BSc Mathematics with Economics. Available to General Course students and as an outside option.

MA100 and ST102. ST202 is also recommended. Students should be prepared to use computer packages when required. Available as an outside option where regulations permit and with permission of the teacher responsible.

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk.

Bayesian Analysis: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and hypothesis testing, Bayes factors and model selection. Comparison with Frequentist approaches.

Implementation: Asymptotic approximations (Laplace approximation, Monte Carlo methods, stochastic simulation), Markov Chain Monte Carlo (MCMC) simulation (Gibbs sampler, Metropolis-Hastings algorithm). Computer tools (R, WinBUGS).

20 hours of lectures and 10 hours of classes in the LT. Two hours of lectures in the ST.
Initial classes will be theoretical. They will be converted to computer workshops including preparation for the project towards the end of the course.

Optional problem sets and computer exercises.

P.M. Lee, Bayesian Statistics. An introduction.
J.O. Berger, Statistical Decision Theory and Bayesian Analysis.
D. Gamerman, H. F. Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference
A. Gelman, Bayesian data analysis.

A project (20%) and two-hour exam in the ST (80%).