Jensen's and Cantelli's Inequalities with Imprecise Previsions

TL;DR

This paper extends Jensen's and Cantelli's inequalities to an imprecise probability framework using lower and upper previsions, providing new bounds and generalizations with applications in uncertainty quantification.

Contribution

It introduces Jensen-like inequalities and generalizes Jensen's inequality within an imprecise probability setting, including applications to Lyapunov's inequality and inferential problems.

Findings

Imprecise Jensen's inequalities are established.

Generalizations of Jensen's inequality are proposed.

Cantelli-like inequalities are derived with improved bounds.

Abstract

We investigate how basic probability inequalities can be extended to an imprecise framework, where (precise) probabilities and expectations are replaced by imprecise probabilities and lower/upper previsions. We focus on inequalities giving information on a single bounded random variable $X$, considering either convex/concave functions of $X$ (Jensen's inequalities) or one-sided bounds such as $(X\geq c)$ or $(X\leq c)$ (Markov's and Cantelli's inequalities). As for the consistency of the relevant imprecise uncertainty measures, our analysis considers coherence as well as weaker requirements, notably $2$-coherence, which proves to be often sufficient. Jensen-like inequalities are introduced, as well as a generalisation of a recent improvement to Jensen's inequality. Some of their applications are proposed: extensions of Lyapunov's inequality and inferential problems. After discussing upper and lower Markov's inequalities, Cantelli-like inequalities are proven with different degrees of consistency for the related lower/upper previsions. In the case of coherent imprecise previsions, the corresponding Cantelli's inequalities make use of Walley's lower and upper variances, generally ensuring better bounds.