K-distribution

About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.

A Remark on Joint Probability Distributions for Quantum Observables

Abstract: A simple formal result concerning joint probability distributions in quantum mechanics is obtained. It is shown that some relatively weak properties of the joint distributions cannot be satisfied in the scope of standard quantum mechanics.

The Distributions of Sums, Products and Ratios of Inverted Bivariate Beta Distribution 1

Abstract: In this paper we derive the exact distributions of sums, products, and ratios of two random variables when they follow the bivariate inverted beta distribution. Forms of the probability density functions of these distributions are presented. The moments of theses distributions are derived. We provide extensive tabulation of the percentiles points associated with the distributions obtained.

Classes of probability distributions and their applications

Abstract: The aim of this paper is a nontrivial application of certain classes of probability distribution functions with further establishing the bounds for the least root of the functional equation x = b G(μ − μx), where b G(s) is the Laplace-Stieltjes transform of an unknown probability distribution function G(x) of a positive random variable having the first two moments g1 and g2, and μ is a positive parameter satisfying the condition μg1 > 1. The additional information characterizing G(x) is that it belongs to the special class of distributions such that the difference between two elements of that class in the Kolmogorov (uniform) metric is not greater than κ. The obtained result is then used to establish the lower and upper bounds for loss probabilities in certain loss queueing systems with large buffers as well as continuity theorems in large M/M/1/n queueing systems.

Statistical Analysis of High Resolution TerraSAR-X Images for Ground Targets Detection

Abstract: TerraSAR-X is a new generation of high-resolution (1 m) space borne Synthetic Aperture Radar (SAR) satellite, which makes ground targets detection (GTD), one of the hot problems in SAR image application fields, possible As for the mostly used CFAR algorithm for GTD, the statistical analysis of SAR image is an essential step in threshold calculation However, it has been proved that the traditionally used Rayleigh distribution is no longer suitable to modelling the statistical characteristics of 1 m resolution TerraSAR-X image with ground targets To model these heavy-tailed distributions, some other distributions, including Gamma, Weibull, lognormal and K distribution models have been applied According to the analysis of large numbers of experiment results, lognormal distribution model is selected to describe the statistical characteristics of the high resolution SAR images with ground targets Then genetic algorithm (GA) is adopted to efficiently estimate the model parameters as accurately as possible, which are subsequently used to calculate the global CFAR detection threshold The experiments indicate that, lognormal distribution model with GA based parameters computation are more suitable than Gamma, Weibull and K distribution models to describe the statistical characteristics of the high resolution SAR images, which is propitious to performance improvement of the global CFAR algorithm

Distributions and Applications Moment Information for Probability Distributions, Without Solving the Moment Problem. I: Where is the Mode?

Abstract: How much information does a small number of moments carry about the unknown distribution function? Is it possible to explicitly obtain from these moments some useful information, e.g., about the support, the modality, the general shape, or the tails of a distribution, without going into a detailed numerical solution of the moment problem? In this paper a theoretical result of Johnson and Rogers is generalized to be valid for all moment problems and is exploited to demonstrate that a few moments are able to provide us with valuable information about the position of the mode of an unknown (unimodal) distribution.