PUBH70061 - Bayesian Reasoning and Methods for Spatio-Temporal Data

Summary

Spatial and spatio-temporal analysis represent an increasingly important tool in public health research as well as in geographic and environmental epidemiology due to the emerging availability of spatial/temporal health data and the development of novel computational techniques, allowing for the analysis of large database.
The module provides a comprehensive introduction to the concepts of Bayesian modelling and inference, and the statistical methods used in analysing spatial and spatio-temporal data. In the first part of the course, students will learn about the main theoretical concepts of the Bayesian approach to probability and inference, before moving on to statistical modelling and interpretation. Successively, they will acquire concepts, methodologies and practical skills to manipulate, effectively visualize and model spatially- and temporally-related data. At the end of this module, students will be able to handle with confidence spatial and/or temporal data, identify patterns of dependence and level of noise in the data, describe and quantify risk of diseases as well as critically interpret and discuss the results from their analyses.

Lecturer and Programming Tutor in Spatial Statistics

Deliver teaching in INLA-SPDE modelling, covering continuous-domain spatio-temporal methods that employ finite element approaches through mesh-based spatial discretisation. Contribute to students’ understanding of both the theoretical underpinnings and practical implementation of advanced spatial statistical models. Lead programming workshops focused on the development of coding proficiency, computational problem-solving, and the application of statistical techniques to real-world datasets. Support student learning by communicating complex concepts clearly and providing targeted practical instruction.