Statistical models based on stochastic partial differential equations


Project Description

The student will learn about modern statistical methods based on stochastic partial differential equations (SPDEs). One important advantage with formulating statistical models using SPDEs is that it facilitates non-Gaussian extensions of several popular Gaussian models. Such extensions are useful for applications where the data has features that cannot be captured by Gaussian models. The goal of the project is to implement and compare these models for applications to longitudinal medical data and spatial environmental data.
Program - Statistics
Division - Computer, Electrical and Mathematical Sciences and Engineering
Field of Study - ​Statistics

About the

David Bolin

Associate Professor, Statistics

David Bolin
Professor Bolin‘s main research interests are in stochastic partial differential equations and their applications in statistics, with a focus on development of practical, computationally efficient tools for modelling non-stationary and non-Gaussian processes. He also works on uncertainty quantification and visualization for applications in spatial statistics and medical imaging.

Desired Project Deliverables

​​Written report and computer code that reproduces the results