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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TL;DR: In this article, the authors present trigonometric functions and cdmblnations from probability distributions and apply them to the problem of statistical computing and simulation in the context of statistical computer vision.
Abstract: (1982). C132. Trigonometric functions and cdmblnations from probability distributions. Journal of Statistical Computation and Simulation: Vol. 15, No. 2-3, pp. 233-237.
10 Jul 2018
TL;DR: A hybrid sea-clutter distribution (KR distribution) with closed-form expression for high-resolution and high grazing angle situation is proposed, which fits the measured data and the method of parameter estimation for KR distribution is given, that is, the geometric segmentation parameter estimation method based on path gain.
Abstract: Many studies show that the sea clutter obeys the K distribution With the continuous improvement of the radar resolution and the increase of the grazing angle, the distribution of sea clutter gradually deviates from the K distribution Although good fitting results can be obtained through the more complicated sea clutter models such as KK distribution, KA distribution, K+Rayleigh distribution and so on, the computational efficiency of the detection algorithm is reduced obviously For computational complexities are caused by large number of parameter estimations and non-closed expression A hybrid sea-clutter distribution (KR distribution) with closed-form expression for high-resolution and high grazing angle situation is proposed, which fits the measured data The method of parameter estimation for KR distribution is given, that is, the geometric segmentation parameter estimation method based on path gain The results show that the fitting effect of KR distribution is better than both of K distribution and Rayleigh distribution under the conditions of high resolution and large grazing angle The fitting effect will be better as the grazing angle increases The parameter estimation method of geometric segmentation based on path gain can find the fitting parameters accurately
01 Jan 2016
TL;DR: In this article, certain further generalizations of the Bell numbers are introduced and applied to defining the respective distributions. But these generalizations have not been applied to the distribution of random counts of Thunderstorm hits.
Abstract: SUMMARY. In the study of some problems in discrete distributions, the Bell numbers and their generalization have received much attention. Here certain further generalizations of the Bell numbers are introduced and applied to defining the respective distributions. Appro priate probabilistic models for the new distributions are considered. A number of properties of them are investigated. The minimum variance unbiased estimator (MVUE) of a parametric function for a two-parameter distribution is considered. In a special case, this distribution is found to give a good fit to a distribution of random counts of Thunderstorm hits. For other distributions quite a few probability tables are given.
TL;DR: In this paper, a discrete time branching process is considered in which the offspring distribution alternates between two known probability distributions, and it is shown that for any pair of distributions, the probability of ultimate extinction is reduced if the process with the smaller individual probability of extinction starts first.
Abstract: A discrete time branching process is considered in which the offspring distribution alternates between two known probability distributions. There are two cases to consider, depending on which of the probability distributions initiates the process. It is shown, both graphically and analytically that for any pair of distributions, the probability of ultimate extinction is reduced if the process with the smaller individual probability of extinction starts first. An example is given in the case when the offspring distribution alternates between two geometric distributions.