11/22/2023 0 Comments Transcribe 9.30 instaling![]() The course will be delivered through a combination of lectures and computer-based practical sessions. Throughout the course there will be practical examples from epidemiology, public health, environmental and social sciences fields. Finally, we will describe how to use R-INLA for more advanced problems in the spatio-temporal realm, for instance how to deal with misaligned data. We will then extend this to deal with spatio-temporal data. Moving on to geostatistical data we will introduce the stochastic partial differential equation (SPDE) approach, used for spatial modelling on a continuous field. Following that, we will extend the approach to include temporal dependency and touch briefly on spatio-temporal interactions. Then we will move to the core of the course, by focusing on area level data and presenting how to model spatially structured random effects through conditional autoregressive specifications. We will also introduce elements for geocomputation with R. ![]() We will first go through the basics of Bayesian inference and will then learn how to model hierarchical structures. Specific focus will be given to Bayesian inference through the Integrated Nested Laplace Approximation (INLA) approach. This short course will provide a comprehensive introduction to concepts, methods, and R tools for geospatial data analytics, which involves collecting, exploring, modelling, and visualising data that exhibit dependencies in space and/or time. ![]()
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