Results 11 to 20 of about 11,188 (189)

Approximation of Free Convolutions by Free Infinitely Divisible Laws [PDF]

Theory of Probability & Its Applications, 2021
Основываясь на методе сyбoрдинационных функций, мы получаем оценки минимальных ошибок аппроксимации $n$-кратных свободных сверток вероятностных мер безгранично делимыми свободными вероятностными мерами.
Chistyakov, Gennadiy, Götze, Friedrich
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A note on regularity for free convolutions [PDF]

Annales de l'Institut Henri Poincare (B) Probability and Statistics, 2006
Abstract Let μ ⊞ ν and μ ⊠ ν denote the free additive convolution and the free multiplicative convolution, respectively, of the Borel probability measures μ and ν. We analyze the boundary behavior of the functions G μ ⊞ ν ( z ) = ∫ 1 z − t d ( μ ⊞ ν ) ( t ) and ψ μ ⊠ ν ( z ) = ∫ z
Belinschi, Serban Teodor
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On the support of free convolutions

We extend to arbitrary measures results of Bao, Erdös, Schnelli, Moreillon, and Ji on the connectedness of the supports of additive convolutions of measures on \mathbb{R} and of free multiplicative convolutions of measures on \mathbb{R}_+. More precisely, the convolution of two measures with connected supports also has connected support.
Belinschi, Serban, Bercovici, Hari, Ho, Ching-Wei   +2 more
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The Hadamard product and the free convolutions [PDF]

Statistics & Probability Letters, 2017
It is shown that if a probability measure $ν$ is supported on a closed subset of $(0,\infty)$, that is, its support is bounded away from zero, then the free multiplicative convolution of $ν$ and the semicircle law is absolutely continuous with respect to the Lebesgue measure.
Arijit Chakrabarty, Chakrabarty, Arijit
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Computing free convolutions via contour integrals

Random Matrices: Theory and Applications
This work proposes algorithms for computing additive and multiplicative free convolutions of two given probability measures. We consider probability measures with compact support whose free convolution results in a probability measure with a density function that exhibits a square-root decay at the boundary (for example, the semicircle distribution or
Alice Cortinovis, Lexing Ying
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The arithmetic of distributions in free probability theory

Open Mathematics, 2011
Chistyakov G, Götze F. The arithmetic of distributions in free probability theory. Central European Journal of Mathematics. 2011;9(5):997-1050.We give an analytical approach to the definition of additive and multiplicative free convolutions which is ...
Chistyakov Gennadii, Götze Friedrich
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DMPCONV: DECOUPLING MULTI-BRANCH POINTWISE CONVOLUTIONS FOR LIGHT-WEIGHT REMOTE SENSING SCENE CLASSIFICATION [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022
The use of multi-branch architectures in off-the-shelf light-weight residual series neural networks can significantly improve their performance in remote sensing scene classification tasks.
J. Hou, J. Hou, J. Hou, J. Hou, Z. Guo, Z. Guo, Y. Wu, Y. Wu, W. Diao, W. Diao, Y. Feng, Y. Feng, Y. Feng, Y. Feng   +13 more
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Adaptive Image Dehazing Algorithm Based on Dynamic Convolution Kernels [PDF]

Jisuanji kexue, 2023
Existing image dehazing methods generally have problems such as incomplete dehazing and color distortion.Image dehazing methods based on traditional deep learning models mostly use static inference during testing,which use the same and fixed parameters ...
LIU Zhe, LIANG Yudong, LI Jiaying
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Cumulants for finite free convolution [PDF]

Journal of Combinatorial Theory, Series A, 2018
In this paper we define cumulants for finite free convolution. We give a moment-cumulant formula and show that these cumulants satisfy desired properties: they are additive with respect to finite free convolution and they approach free cumulants as the dimension goes to infinity.
Octavio Arizmendi, Daniel Perales
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A Context-Free Grammar Associated with Fibonacci and Lucas Sequences

Journal of Mathematics, 2023
We introduce a context-free grammar G=s⟶s+d,d⟶s to generate Fibonacci and Lucas sequences. By applying the grammar G, we give a grammatical proof of the Binet formula.
Harold Ruilong Yang
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