A parameterization of anisotropic Gaussian fields with penalized complexity priors

Liam Elias (School of Mathematics, University of Edinburgh)

Tuesday 10th December, 2024 15:00-16:00 Room 110

Abstract

Gaussian random fields (GFs) are fundamental tools in spatial modelling and can be represented flexibly and efficiently as solutions to stochastic partial differential equations (SPDEs). The SPDEs depend on specific parameters, which enforce various field behaviours and can be estimated using Bayesian inference. However, the likelihood typically only provides limited insights into the covariance structure under in-fill asymptotics. In response, it is essential to leverage priors to achieve appropriate, meaningful covariance structures in the posterior. This study introduces a smooth, invertible parameterization of the correlation length and diffusion matrix of an anisotropic GF and constructs penalized complexity (PC) priors for the model when the parameters are constant in space. The formulated prior is weakly informative, effectively penalizing complexity by pushing the correlation range toward infinity and the anisotropy to zero.

Add to your calendar

Download event information as iCalendar file (only this event)