# Reparameterization of extreme value framework for improved Bayesian workflow

arXiv: https://arxiv.org/abs/2210.05224 · HTML: https://mlsanity.com/paper/2210.05224/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization](https://mlsanity.com/paper/2402.16748/) (2024)
- [Gradient boosting for extremes: sampling theory and application to insurance](https://mlsanity.com/paper/2606.14268/) (2026)

## Abstract (arXiv)

Using Bayesian methods for extreme value analysis offers an alternative to frequentist ones, with several advantages such as easily dealing with parametric uncertainty or studying irregular models. However, computations can be challenging and the efficiency of algorithms can be altered by poor parametrization choices. The focus is on the Poisson process characterization of univariate extremes and outline two key benefits of an orthogonal parameterization. First, Markov chain Monte Carlo convergence is improved when applied on orthogonal parameters. This analysis relies on convergence diagnostics computed on several simulations. Second, orthogonalization also helps deriving Jeffreys and penalized complexity priors, and establishing posterior propriety thereof. The proposed framework is applied to return level estimation of Garonne flow data (France).