Traffic distributions and independence: permutation invariant random matrices and the three notions of independence
Camille Male

TL;DR
This paper extends free probability to traffic probability, analyzing permutation invariant random matrices and unifying tensor, free, and Boolean independence through traffic distributions and a new form of independence.
Contribution
It introduces traffic distributions for random matrices, extends free probability, and unifies three notions of independence within a broader traffic space framework.
Findings
Wigner, Haar unitary, and permutation matrices converge in traffic distributions.
Traffic independence generalizes tensor, free, and Boolean independence.
New results on limiting *-distributions of constructed matrices.
Abstract
Voiculescu's notion of asymptotic free independence is known for a large class of random matrices including independent unitary invariant matrices. This notion is extended for independent random matrices invariant in law by conjugation by permutation matrices. This fact leads naturally to an extension of free probability, formalized under the notions of traffic probability. We first establish this construction for random matrices. We define the traffic distribution of random matrices, which is richer than the *-distribution of free probability. The knowledge of the individual traffic distributions of independent permutation invariant families of matrices is sufficient to compute the limiting distribution of the join family. Under a factorization assumption, we call traffic independence the asymptotic rule that plays the role of independence with respect to traffic distributions. Wigner…
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