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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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Book ChapterDOI
TL;DR: This work states that different classes of these distributions are appropriate for discrete and continuous data, and generation of random numbers from these distributions provides the basis for statistical simulation.
Abstract: Parametric distributions are theoretical mathematical forms, which are often useful for compactly representing variations and uncertainty in data. Different classes of these distributions are appropriate for discrete and continuous data. These characterizations can provide the mathematical structure for inferences about the data-generating processes. Generation of random numbers from these distributions provides the basis for statistical simulation.

6 citations

Book ChapterDOI
01 Jan 1982
TL;DR: In this article, an analogous problem was considered by compounding a binomial distribution and a generalized beta one given in [8] by T.G.Środka.
Abstract: In paper [6] J.G. Skellam gave a distribution which grew out of a compound of a binomial distribution and a beta one. In the present paper we consider an analogous problem by compounding a binomial distribution and a generalized beta one given in [8] by T. Środka. The distribution obtained yields interesting special as well as limit cases constituting compounds of the binomial distribution and those most frequently encountered in statistical practice.

6 citations

Proceedings ArticleDOI
03 Apr 2006
TL;DR: The Pearson system of distributions is studied as a means to obtain approximate expressions for such distributions and is illustrated for a number of diversity techniques employed on Nakagami and Weibull fading channels.
Abstract: In performance analyses of wireless communication systems, expressions for the probability distributions of certain random variables are often needed. While exact, efficiently computable closed-form expressions are desirable, they are often very difficult, if at all possible, to obtain. In this paper, the Pearson system of distributions is studied as a means to obtain approximate expressions for such distributions. The usefulness of this approach is illustrated for a number of diversity techniques employed on Nakagami and Weibull fading channels. The resulting relatively simple approximations are shown to be very accurate

6 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider the problem of finite support phase type distributions (FSPH) and derive the EM algorithms for two classes of FSPH, the first of which is the class of matrix exponential distributions dense in (a, b).
Abstract: This research is motivated by the fact that many random variables of practical interest have a finite support. For fixed a < b, we consider the distribution of a random variable X = (a + Ymod(b − a)), where Y is a phase type (PH) random variable. We demonstrate that as we traverse for Y the entire set of PH distributions (or even any subset thereof like Coxian that is dense in the class of distributions on [0, ∞)), we obtain a class of matrix exponential distributions dense in (a, b). We call these Finite Support Phase Type Distributions (FSPH) of the first kind. A simple example shows that though dense, this class by itself is not very efficient for modeling; therefore, we introduce (and derive the EM algorithms for) two other classes of finite support phase type distributions (FSPH). The properties of denseness, connection to Markov chains, the EM algorithm, and ability to exploit matrix-based computations should all make these classes of distributions attractive not only for applied probability but als...

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
20228
20213
20207
201914
201816