Results 1 to 10 of about 16,494 (150)
Understanding Hierarchical Processes [PDF]
Entropy, 2022 Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks.
Wray Buntine
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A New First-Order Integer-Valued Autoregressive Model with Bell Innovations [PDF]
Entropy, 2021 A Poisson distribution is commonly used as the innovation distribution for integer-valued autoregressive models, but its mean is equal to its variance, which limits flexibility, so a flexible, one-parameter, infinitely divisible Bell distribution may be ...
Jie Huang, Fukang Zhu
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On the infinite divisibility of distributions of some inverse subordinators
Modern Stochastics: Theory and Applications, 2018 We consider the infinite divisibility of distributions of some well-known inverse subordinators. Using a tail probability bound, we establish that distributions of many of the inverse subordinators used in the literature are not infinitely divisible.
Arun Kumar, Erkan Nane
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The infimum values of two probability functions for the Gamma distribution [PDF]
Journal of Inequalities and ApplicationsLet α, β be positive real numbers and let X α , β $X_{\alpha ,\beta}$ be a Gamma random variable with shape parameter α and scale parameter β. We study infimum values of the function ( α , β ) ↦ P { X α , β ≤ κ E [ X α , β ] } $(\alpha ,\beta )\mapsto P\{
Ping Sun, Ze-Chun Hu, Wei Sun
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Tempered infinitely divisible distributions and processes [PDF]
Theory of Probability & Its Applications, 2011 In this paper, we construct the new class of tempered infinitely divisible (TID) distributions. Taking into account the tempered stable distribution class, as introduced by in the seminal work of Rosinsky , a modification of the tempering function allows
Bianchi, Michele Leonardo +3 more
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Characteristic Kernels and Infinitely Divisible Distributions [PDF]
J. Mach. Learn. Res., 2016 We connect shift-invariant characteristic kernels to infinitely divisible distributions on $\mathbb{R}^{d}$. Characteristic kernels play an important role in machine learning applications with their kernel means to distinguish any two probability ...
Fukumizu, Kenji, Nishiyama, Yu
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More on Equivalence of Infinitely Divisible Distributions
Annals of Probability, 1975 Any infinitely divisible distribution on $R^n$ with infinite absolutely continuous Levy measure and no Gaussian component has a density which is positive a.e. over its support.
Hudson, W. N., Mason, J. D.
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On Strongly Unimodal Infinitely Divisible Distributions
Annals of Probability, 1982 There are many results as to unimodality of infinitely divisible distributions. But few are known about strong unimodality of infinitely divisible distributions. In this paper, we consider two subclasses of infinitely divisible distributions and give necessary and sufficient conditions for strong unimodality of distributions belonging to such classes.
Makoto Yamazato
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Educational approaches and research achievements of Alexander Khintchine in mathematics [PDF]
ریاضی و جامعه, 2023 The role of Alexander Yakovlevich Khintchine in mathematics and especially in probability theory is undeniable. His approaches to teaching mathematics, especially in the secondary school, are still a perfect model for mathematics teachers.
Ramin Kazemi
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A New Bound in the Littlewood–Offord Problem
Mathematics, 2022 The paper deals with studying a connection of the Littlewood–Offord problem with estimating the concentration functions of some symmetric infinitely divisible distributions.
Friedrich Götze, Andrei Yu. Zaitsev
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