Bayesian approach to extreme-value statistics based on conditional maximum-entropy method

TL;DR

This paper explores the application of the conditional maximum-entropy method (C-MaxEnt) to extreme-value statistics, demonstrating how it can produce priors consistent with Jeffreys' rule, exemplified through Weibull distributions.

Contribution

It introduces the use of C-MaxEnt for selecting priors in extreme-value Bayesian analysis, providing a simple and effective approach.

Findings

C-MaxEnt can generate priors satisfying Jeffreys' rule for Weibull distributions

The method simplifies prior selection in extreme-value Bayesian statistics

Demonstrates the applicability of C-MaxEnt to specific distribution types

Abstract

Recently, the conditional maximum-entropy method (abbreviated as C-MaxEnt) has been proposed for selecting priors in Bayesian statistics in a very simple way. Here, it is examined for extreme-value statistics. For the Weibull type as an explicit example, it is shown how C-MaxEnt can give rise to a prior satisfying Jeffreys' rule.