2016
DOI: 10.1214/15-aihp691
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Independences and partial $R$-transforms in bi-free probability

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Cited by 25 publications
(42 citation statements)
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“…Obviously our result here complements the recent work on operations on bi-free bi-partite hermitian two-faced pairs ( [10], [7], [5], [11], [8]). …”
Section: Introductionsupporting
confidence: 84%
“…Obviously our result here complements the recent work on operations on bi-free bi-partite hermitian two-faced pairs ( [10], [7], [5], [11], [8]). …”
Section: Introductionsupporting
confidence: 84%
“…Due to the recent work ( [10], [7], [1], [2], [8], [3], [5]) we already have a much better understanding of bi-free probability.…”
Section: Introductionmentioning
confidence: 99%
“…The additive and multiplicative cases being closely related in [6], we will be able to make also substantial use here of our work in [10] on the bi-free partial R-transform. Since a combinatorial proof for the partial R-transform was found in [8] and there is also combinatorial work for handling multiplication of bi-free systems of variables using additive bi-free cumulants [1] it is to be expected that a combinatorial approach to the 2-variables partial Sand T -transforms we consider here is possible. On the other hand, the study of infinite divisibility for additive bi-free convolution of probability measures on R 2 in [5] may not have an immediate analogue for the operations considered here, since the result of the operations ⊠⊠ and ⊞⊠ on probability measures seems to produce signed measures only in general.…”
Section: Introductionmentioning
confidence: 99%
“…A two-faced family a = ((a i ) i∈I , (a j ) j∈J ) in a non-commutative probability space (A, ϕ) has a bi-Boolean central limit distribution (or centred bi-Boolean Gaussian distribution) with covariance matrix C if there exists a complex matrix C = (C k,ℓ ) k,ℓ∈I⊔J such that The algebraic bi-Boolean central limit theorem immediately follows from [11, Theorem 6.2] with the given sequence being bi-Boolean independent and the limiting distribution being a centred bi-Boolean Gaussian distribution. Similarly, it is easy to see that a Kac/Loeve type theorem also holds in the bi-Boolean setting (see [22,Theorem 3.2] for the bi-free version) and a general bi-Boolean limit theorem can be deduced from [11, Theorem 6.5], which we record as follows without proof.…”
Section: Combinatorial Aspectsmentioning
confidence: 96%