The study of the theory and practice of statistical methods involving observations of many variables. Special emphasis will be given to two problems. The first will address the inferential problems that arise when data consists of simultaneous measurements on several correlated variables. The second will address the construction and analysis of time series models. The course will be illustrated throughout by real examples, from medicine, industry and government using statistical packages and specially designed software.
Teaching is by lecture and seminars given in the first two terms.
The syllabus varies. Students would currently choose from some of the sections given below. Eliciting and modelling prior information, idiot Bayes, export systems, utility and coherent inference. Graphical methods, Prior to posterior analyses for the normal, binomial or multinomial, Probabilistic inference on networks. Markov property and an introduction to stochastic control, adapted policies, the Bellman equation, finite horizon control problems, optimal stopping, negative dynamic programming, bandit problems, dynamic allocation indices.