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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01 Oct 1955
TL;DR: In this article, the probability distribution of the instantaneous resultant amplitude of the sum of a constant vector and a Rayleigh-distributed vector is discussed. But the authors emphasize that two distributions are required to describe the Rayleigh distribution of a vector: the distribution of its amplitude and its phase, and that these distributions may be used to describe random variables occurring in ionospheric and irregular terrain propagation problems.
Abstract: Formulas, tables, and graphs are given for the probability distribution of the instantaneous resultant amplitude of the sum of a constant vector and a Rayleigh-distributed vector. It is emphasized that two distributions are required to describe a Rayleigh-distributed vector: the distribution of its amplitude and the distribution of its phase. A summary is presented of physical conditions which must be satisfied for a given phenomenon to exhibit statistical properties of a Rayleigh-distributed vector. References are made to ways in which these distributions may be used to describe random variables occurring in ionospheric, tropospheric, and irregular terrain propagation problems. Finally, a discussion is given of amplitude and phase distributions of two other random vectors encountered in tropospheric propagation studies.
109 citations
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30 Aug 2008TL;DR: In this paper, Lagrangian Probability Distributions (LPD) models are used to estimate the probability of Lagrangians for a given set of properties. But the Lagrange Probability Models (LPM) model is not suitable for general LPDs.
Abstract: * Lagrange Bio * Dedication * Foreword * Preface * List of Tables * Abbreviations * Preliminary Information * Lagrangian Probability Distributions * Properties of General Lagrangian Distributions * Quasi-Probability Models * Some Urn Models * Development of Models and Applications * Modified Power Series Distributions * Some Basic Lagrangian Distributions * Generalized Poisson Distribution * Generalized Negative Binomial Distribution * Generalized Logarithmic Series Distribution * Lagrangian Katz Distribution * Random Walks and Jumps Models * Bivariate Lagrangian Distributions * Multivariate Lagrangian Distributions * Computer Generation of Lagrangian Variables * References * Index
108 citations
TL;DR: An iterative method is presented which gives an optimum approximation to the Joint probability distribution of a set of binary variables given the joint probability distributions of any subsets of the variables (any set of component distributions).
Abstract: An iterative method is presented which gives an optimum approximationto the joint probability distribution of a set of binary variables given the joint probability distributions of any subsets of the variables (any set of component distributions) The most significant feature of this approximation procedure is that there is no limitation to the number or type of component distributions that can be employed Each step of the iteration gives an improved approximation, and the procedure converges to give an approximation that is the minimum information (ie maximum entropy) extension of the component distributions employed
107 citations
TL;DR: A comprehensive survey of the different methods of generating discrete probability distributions as analogues of continuous probability distributions is presented along with their applications in construction of new discrete distributions.
Abstract: In this paper a comprehensive survey of the different methods of generating discrete probability distributions as analogues of continuous probability distributions is presented along with their applications in construction of new discrete distributions. The methods are classified based on different criterion of discretization.
105 citations