Ó. Rodriguez de Rivera Ortega
The potential for statistical complexity in ecological models has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data.
Here, we will introduce a joint point process approach, which uses multiple Gaussian random fields to represent ecological dynamics in a spatio-temporal model. We will present different ecological applications, from "simple" spatial interactions, to spatio-temporal analysis of hazards or dispersion process, in order to understand the flexibility of this approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with \texttt{inlabru}, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used to understand complex ecological system.
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Programado
Sesión Invitada Royal Statistical Society
9 de junio de 2022 10:10
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