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On a Likely Shape of the Random Ferrers Diagram

✍ Scribed by Boris Pittel


Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
513 KB
Volume
18
Category
Article
ISSN
0196-8858

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✦ Synopsis


We study the random partitions of a large integer n, under the assumption that all such partitions are equally likely. We use Fristedt's conditioning device which Ž . connects the parts summands distribution to the one of a g-sequence, that is, a sequence of independent random variables, each distributed geometrically with a size-dependent parameter. Confirming a conjecture made by Arratia and Tavare, ẃe prove that the joint distribution of counts of parts with size at most s < n 1r 2 n Ž 1r 2 . at least s 4 n , resp. is closeᎏin terms of the total variation distanceᎏto the n Ž distribution of the first s components of the g-sequence of the g-sequence minus n . the first s y 1 components, resp. . We supplement these results with the estimates n for the middle-sized parts distribution, using the analytical tools revolving around the Hardy᎐Ramanujan formula for the partition function. Taken together, the estimates lead to an asymptotic description of the random Ferrers diagram, close to the one obtained earlier by Szalay and Turan. As an application, we simplify ćonsiderably and strengthen the Szalay᎐Turan formula for the likely degree of an írreducible representation of the symmetric group S . We show further that both n the size of a random conjugacy class and the size of the centraliser for every element from the class are doubly exponentially distributed in the limit. We prove that a continuous time process that describes the random fluctuations of the diagram boundary from the deterministic approximation converges to a Gaussian Ž . non-Markov process with continuous sample path. Convergence is such that it implies weak convergence of every integral functional from a broad class. To demonstrate applicability of this general result, we prove that the eigenvalue distribution for the Diaconis᎐Shahshahani card-shuffling Markov chain is asymptotically Gaussian with zero mean, and variance of order n y3 r2 .


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