Course co-ordinator(s): Dr Simon Malham (Edinburgh).
The aims of this course are to develop techniques of data assimilation in mathematical biology, ecology and medicine. This will be achieved by a mixture of lectures on basic methodology, tutorial exercises, computer labs, case studies and a large group-based modelling, implementation and simulation project. We will introduce a number of data assimilation approaches that are widely used in applications to mathematical biology, ecology and medicine, including basic regression analysis, variational approaches, Kalman filtering, extended and ensemble Kalman filtering and the Bayesian inference approach. The course will teach practical implementation of these data assimilation techniques in the context of computer simulations, which will be illustrated by prototype applications from predictive modelling in biology, ecology and medicine. These methodologies will form the basis for a series of modelling case studies as well as the group-based project component of the course.
Further information: Syllabus, Number of lectures, Assessment, Learning Outcomes.
SCQF Level: 11.