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Advances in Mathematics
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Advances in Mathematics
Article . 1998
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Advances in Mathematics
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Asymptotic Estimates Using Probability

Asymptotic estimates using probability
Authors: Beckner, William; Regev, Amitai;

Asymptotic Estimates Using Probability

Abstract

The authors use probabilistic arguments in the spirit of the De Moivre-Laplace central limit theorem to obtain asymptotic estimates for combinatorial sums. The main question under discussion is the following. For a given sequence \(\chi_n=\sum_{\lambda\vdash n}f(\lambda)\chi_{\lambda}\) of \(S_n\)-characters defined in terms of the associated Young diagrams and with known asymptotics of the degrees when \(n\) tends to infinity, increase the length of the columns of the Young diagrams by a fixed factor \(q\), i.e. replace the partitions \(\lambda=(\lambda_1,\ldots,\lambda_k)\) with the new partitions \(q\ast\lambda= (\lambda_1^q,\ldots,\lambda_k^q)= (\lambda_1,\ldots,\lambda_1,\ldots, \lambda_k,\ldots,\lambda_k)\). Then determine the changes that occur in the asymptotics of the degrees of the new sequence \(\chi_{qn}= \sum_{\lambda\vdash n}f(\lambda)\chi_{q\ast\lambda}\) of \(S_{qn}\)-characters. The authors determine such estimates for the so-called Young derived sequences of characters. As a result they obtain some rather interesting integration formulas. In particular, some of the integrals involved are extensions of Selberg and Dyson-Macdonald-Mehta type integrals.

Keywords

Mathematics(all), Combinatorial probability, characters of symmetric groups, Young diagrams, Exact enumeration problems, generating functions, central limit theorem, probabilistic arguments, Central limit and other weak theorems, asymptotic estimates, Asymptotic enumeration, combinatorial sums, Young derived character sequences, Combinatorial aspects of representation theory, Combinatorial identities, bijective combinatorics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
hybrid