Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

The students completing the module will have gained knowledge about a number of topics from the statistical sciences at an advanced level. The list may include, but is not limited to, the following topics:

Learning objectives

Knowledge

• state space models and hidden Markov models
• expectation-maximisation (EM) algorithm and missing data
• multivariate Gaussian distribution (and related distributions, e.g. Hotelling's T² and Wishart distributions)
• INLA
• generalised estimating equations
• bootstrap, cross-validation and other resampling techniques

Skills

• can apply the relevant methodologies to one or more datasets by using appropriate software implementations, and interpret the output and modify the model parameters accordingly
• are able state the underlying assumptions and argue about limitations and extendibility of the methodology in one or more specific settings
• can assess goodness-of-fit for the models where appropriate

Competences

• can acquire supplementary knowledge about the relevant methodologies
• can combine appropriate topics from the course to analyse a specific dataset
• can in writing describe the methodologies, results and outcome from an analysis of a specific dataset

Extent and expected workload

This is a 5 ECTS course module and the work load is expected to be 150 hours for the student.
 

Exam

Exams