Fast Approximate Inference for Spatial Extreme Value Models
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The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process on the GEV parameters. Inference for such hierarchical GEV models is typically carried out using Markov chain Monte Carlo (MCMC) methods. However, MCMC can be prohibitively slow and computationally intensive when the number of latent variables is moderate to large. In this paper, we develop a fast Bayesian inference method for spatial GEV models based on the Laplace approximation. Through simulation studies, we compare the speed and accuracy of our method to both MCMC and a more sophisticated but less flexible Bayesian approximation. A case study in forecasting extreme wind speeds is presented.
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