# Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions

## TL;DR

This paper introduces sub-Weibull distributions, a new class that extends sub-Gaussian and sub-Exponential distributions to heavier tails, with applications in Bayesian deep learning.

## Contribution

It defines the sub-Weibull class, characterizes its properties, and proposes an estimation method for the tail parameter.

## Key findings

- Sub-Weibull distributions encompass heavier tails than sub-Gaussian and sub-Exponential distributions.
- A parameter estimation procedure is developed and demonstrated.
- Application to Bayesian deep learning illustrates practical utility.

## Abstract

We propose the notion of sub-Weibull distributions, which are characterised by tails lighter than (or equally light as) the right tail of a Weibull distribution. This novel class generalises the sub-Gaussian and sub-Exponential families to potentially heavier-tailed distributions. Sub-Weibull distributions are parameterized by a positive tail index $\theta$ and reduce to sub-Gaussian distributions for $\theta=1/2$ and to sub-Exponential distributions for $\theta=1$. A characterisation of the sub-Weibull property based on moments and on the moment generating function is provided and properties of the class are studied. An estimation procedure for the tail parameter is proposed and is applied to an example stemming from Bayesian deep learning.