Reparameterization of extreme value framework for improved Bayesian workflow

Théo Moins, Julyan Arbel, Stéphane Girard, Anne Dutfoy

Abstract: Highlights•An orthogonal parameterization is beneficial for Bayesian inference of extreme value models.•Convergence diagnostics (autocorrelation, ESS, and local Rˆ) are superior in the three maximum domains of attraction.•Jeffreys and PC priors can be computed for the Poisson process model for extremes, and the corresponding posterior is proper.•The return level credible interval length can be reduced by adding prior information on the extreme value index.