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EVAChallenge2019

R code to reproduce the analysis in "Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures" by Daniela Castro-Camilo, Linda Mhalla and Thomas Opitz.

The article proposes a general method for probabilistic prediction of extreme value hot-spots in a spatio-temporal setting, with an application to the Sea Surface Temperature anomalies provided in the data challenge of the Extreme Value Analysis conference 2019.

Data are available upon request to the authors.

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