Probability and Stochastic Processes
Introduction to probability, Markov processes, Poisson processes and their use for modelling. The evaluation of complex stochastic properties via simulation.
Linear programming, mixed-integer programming, heuristics for large scale problems, stochastic programming, stochastic dynamic programming.
Model-based (likelihood) inference for generalised linear models and stochastic processes and model diagnostics, randomisation methods for non-parametric testing.
Modelling for planning and decision support, systems ideas including complexity and feedback, stochastic discrete event simulation, output analysis with model validation, computational challenges including parallelisation.
Bayesian inference, prediction and decision making. Contrasts between Bayesian and classical statistics.
Computational Intensive Methods
|Conjugate analyses, importance sampling approximations, and MCMC for analysing complex stochastic systems.|
Modelling and Problem Solving
Skills for Research and Industry
Presentation skills for non-technical and technical talks/posters/web design. Computer skills including programming in R and Visual Basic.
Skills for eliciting relevant background to problems through to conceptualising these in a model formulation which integrates the relevant scientific knowledge with STOR methods which capture an appropriate level of assumption.
Industrial Problem Solving Days
A current open industrial problem will be presented to the students in groups which are facilitated by staff and current students. An outline approach or solution will be developed for presentation to the collaborator.
Overview of Research Areas
Overview Presentations on thriving research areas in STOR. Students will be expected to produce a summary and a brief literature review.
PhD Research Proposal
Literature review, preliminary study and development of a firm plan for the PhD.