• Corpus ID: 238856716

Fast Approximate Inference for Spatial Extreme Value Models

@inproceedings{Chen2021FastAI,
  title={Fast Approximate Inference for Spatial Extreme Value Models},
  author={Mei-Ching Chen and Reza Ramezan and Martin Lysy},
  year={2021}
}
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… 

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