The BRS-inequality and its Applications A Survey
F. Thomas Bruss

TL;DR
This survey reviews the BRS-inequality, a versatile probabilistic tool providing bounds on the expected maximum sum of non-negative random variables without independence assumptions, with numerous applications across probability and optimization.
Contribution
It compiles and explains the BRS-inequality and its key versions, illustrating its broad applicability through diverse examples and applications in applied probability.
Findings
BRS-inequality provides a universal upper bound without independence assumptions.
Applications include comparisons of iid and dependent variables, and resource allocation problems.
The inequality simplifies analysis in various probabilistic and combinatorial contexts.
Abstract
This article is a survey of results concerning an inequality, which may be seen as a versatile tool to solve problems in the domain of Applied Probability. The inequality, which we call BRS-inequality, gives a convenient upper bound for the expected maximum number of non-negative random variables one can sum up without exceeding a given upper bound One fine property of the BRS-inequality is that it is valid without any hypothesis aboutindependence of the random variables. Another welcome feature is that, once one sees that one can use it in a given problem, its application is often straightforward or, not very involved. This survey is focussed, and we hope that it is pleasant and inspiring to read. The focus is easy to achieve, given that BRS-inequality and its most useful versions can be displayed in three Theorems, one Corollary, and their proofs. We try to do this in an…
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Taxonomy
TopicsRandom Matrices and Applications · Stochastic processes and statistical mechanics · Advanced Combinatorial Mathematics
