Mitchell Lecture 2022: Modelling non-Gaussian spatio-temporal processes

Professor Alexandra M. Schmidt (McGill University)

Wednesday 27th April 16:00-17:00 Online


The School of Mathematics and Statistics is delighted to invite you to the Mitchell Lecture 2022, which will be held on Wednesday 27th April 2022, 16:00 - 17:00 BST via Zoom.

The lecture will be given by Professor Alexandra M. Schmidt, who holds a Professorship of Biostatistics and the endowed University Chair in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University in Montreal, Canada. Her main research interests involve the modelling of spatial and spatio-temporal processes under the Bayesian framework.

An overview of her lecture, entitled Modelling non-Gaussian spatio-temporal processes, is summarised in the abstract below:

In the analysis of most spatiotemporal processes in environmental studies, observations present distributions that are not normal. Commonly, some transformation is applied to the data and inference is performed at the transformed scale. Commonly, the transformation will have an impact on the description of the uncertainty at future instants of time or unobserved locations of interest.

In this talk I will discuss some of the projects I have been involved with in the last five years that relax the assumption of normality of spatiotemporal processes after some suitable transformation of the data. In particular, I will  focus on a recent proposal that models the variance law of multivariate dynamic linear models. The proposed approach adds flexibility to the usual Multivariate Dynamic Gaussian model by defining the process as a scale mixture between a Gaussian and log-Gaussian processes. The scale is represented by a process varying smoothly over space and time which is allowed to depend on covariates. Analysis of artificial datasets show that the parameters are identifiable and simpler models are well recovered by the general proposed model. The analyses of two important environmental processes, maximum temperature and maximum ozone, illustrate the effectiveness of our proposal in improving the uncertainty quantification in the prediction of spatio-temporal processes.

To attend, please register in advance at

We are looking forward to having you.


A recording of this lecture is available here.

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