Time Series - MATH97185
Aims
An introduction to the analysis of time series (series of observations, usually evolving in time) is given which gives weight to both the time domain and frequency domain viewpoints. Important structural features (e.g. reversibility) are discussed, and useful computational algorithms and approaches are introduced. The course is self-contained.
Role
Lecturer
Topics in Advanced Statistics: Graphical Models - MATH97080
Aims
Graphical modelling for both (a) a vector of random variables, and (b) vector-valued time series. Conditional
independence. Dependence structure and graphical representation. Markov properties. Conditional
independence graphs. Decomposable models. Graphical Gaussian models. Model selection. Directed acyclic
graphs (DAGs), Bayesian networks. Graphical modelling of time series (model selection, Kullback-Leibler
approach). (Some prior knowledge of time series analysis would be helpful for part (b2), the last section.)
Role
Lecturer
Time Series - MATH97084
Aims
An introduction to the analysis of time series (series of observations, usually evolving in time) is given which gives weight to both the time domain and frequency domain viewpoints. Important structural features (e.g. reversibility) are discussed, and useful computational algorithms and approaches are introduced. The module is self-contained.
Role
Lecturer