Topic
K-distribution
About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.
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3 citations
TL;DR: This work obtains precise values for both the maximum and minimum variational distance between X and another random variable Y under which an α-coupling of these random variables is possible.
Abstract: Let X be a discrete random variable with a given probability distribution. For any α, 0 ≤ α ≤ 1, we obtain precise values for both the maximum and minimum variational distance between X and another random variable Y under which an α-coupling of these random variables is possible. We also give the maximum and minimum values for couplings of X and Y provided that the variational distance between these random variables is fixed. As a consequence, we obtain a new lower bound on the divergence through variational distance.
3 citations
10 Aug 1988
TL;DR: In this paper, a plausible physical model for the turbulence scattering of an optical wave that would give rise to a two-time scale fluctuation was discussed, and the K distribution, H-K distribution and I-K distributions were analyzed as to the possible scattering conditions, by turbulence, these distributions represent.
Abstract: The statistical fluctuations developed by an optical wave, after passing through atmospheric turbulence, have a non-Gaussian nature. The detected optical intensity appears to have two separate time scales of fluctuations. This paper discusses a plausible physical model for the turbulence scattering of an optical wave that would give rise to a two-time scale fluctuation. The K distribution, H-K distribution and I-K distribution are analyzed as to the possible scattering conditions, by turbulence, these distributions represent.
3 citations
TL;DR: In this article, a polynomial dimensional decomposition method for calculating the probability distributions of random crack-driving forces commonly encountered in elastic-plastic fracture analysis of ductile solids is presented.
Abstract: This paper presents a polynomial dimensional decomposition method for calculating the probability distributions of random crack-driving forces commonly encountered in elastic-plastic fracture analysis of ductile solids. The method involves a hierarchical decomposition of a multivariate function in terms of variables with increasing dimensions, a broad range of orthonormal polynomial bases consistent with the probability measure for Fourier-polynomial expansion of component functions, and an innovative dimension-reduction integration for calculating the expansion coefficients. Unlike the previous development, the new decomposition does not require sample points, yet it generates a convergent sequence of lower-variate estimates of the probability distributions of crack-driving forces. Numerical results, including the probability of fracture initiation of a through-walled-cracked pipe, indicate that the decomposition method developed provides accurate, convergent, and computationally efficient estimates of the probabilistic characteristics of the J-integral. DOI: 10.1115/1.4000159
3 citations
TL;DR: In this article, the inverse of the partition function in 1D Ising model, as a function of the external field, is a product of Fourier transforms of compound geometric distributions, which are random sums (randomly stopped random walks) with the probability of a success depending only on the interaction constant K between sites.
3 citations