## 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