The section EnviBayes is starting a new series of monthly Zoom meetings. The meetings will be devoted to research talks, tutorials, presentation of available data sources and more.

Our first meeting will take place October 22 at 3PM US Eastern time (12PM US Pacific; 9PM US British Standard time and Central European time; 6AM Australian Eastern Standard time).
Title and abstract are provided below.
To register, please use the following link:


Speaker: Alexandra Schmidt, McGill University
Title: Dynamical non-Gaussian modeling of spatial processes

Spatio-temporal processes in environmental applications are often assumed to follow a Gaussian model, possibly after some transformation. However, heterogeneity in space and time might have a pattern that will not be accommodated by transforming the data. In this scenario, modelling the variance laws is an appealing alternative. This worka dds flexibility to the usual Multivariate Dynamic Gaussian model by defining the process as a scale mixture between a Gaussian and log-Gaussian process. The scale is represented by a process varying smoothly over space and time which is allowed to depend on covariates. State-space equations define the dynamics over time for both response and variance processes resulting in feasible inference and prediction. Analysis of artificial datasets show that the parameters are identifiable and simpler models are well recovered by the general proposed model. Two applications to maximum ozone and maximum temperature data illustrate the effectiveness of our proposal in improving the uncertainty quantification in the prediction of spatio-temporal processes. This is joint work with Thaís C. O. Fonseca and Viviana Lobo from the Federal University of Rio de Janeiro, Brazil.