Nadeen graduated from the University of Bath in 2016 with an MMath degree and is particularly interested in statistics.
Nadeen graduated from the University of Bath in 2016 with an MMath degree and is particularly interested in statistics. She completed a summer project on the development of multi-level regression for automation of multi-block mesh generation. As well as mathematics, Nadeen enjoys reading, playing sports and watching films.
Research project title: Bayesian inference for point processes
Supervisor(s): Theresa Smith
Project description: Point patterns, specifically spatial point patterns, occur frequently in the environment sciences and epidemiology. These phenomena are possible to model using point processes from which it is possible to learn about any spatial relationships that cause the point pattern observed as well as stochastic dependence between points in the pattern. In particular, Cox processes (or “doubly stochastic” processes) are practical models when the point pattern is clustering due to environmental heterogeneity that is stochastic. Nadeen is working on computational methods for a particular type of Cox process, log-Gaussian Cox processes (LGCPs), where she builds upon the algorithms currently used for Bayesian inference for LGCPs in order to present an algorithm that can efficiently fit more complex spatial models by sharing data between disjoint regions.
Students joining SAMBa in 2016