Understanding partially observed point processes

​The project is about understanding partially observed high-dimensional point processes, with an aim at diagnosis of side effects of diabetes and chemo therapy.

Spatial point pattern data are more and more often collected in large areas, where one may have hundreds of different species of plants followed over time, or collected for diagnostic purposes, where several (point pattern) samples are taken from several subjects having different conditions (belonging to different groups). Questions related to the data are also getting more complex. Instead of finding a suitable model for one point pattern, model parameters need to be estimated, for example, from data having the hierarchical structure above, i.e. data from different individuals and groups. Furthermore, methods to handle measurement errors and partially observed data are not widely studied in the point process literature. Methods from machine learning, such as clustering and variational Bayes methods, can become very useful, but they need first to be adjusted for spatial point processes.

Published: Mon 29 Apr 2019.