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, a representation for singular distributions in two dimensions is given, which is used to characterize the types of marginal distributions that members of this class can have, and a representation is given that makes the construction of a class of singular distributions of two dimensions simple to carry out.
Abstract: Singular distributions are seldom covered in undergraduate probability courses, although they are of interest in statistics and, as is shown by example, can easily arise through extending mixed discrete and continuous distributions to two or more dimensions. A representation is given that makes the construction of a class of singular distributions in two dimensions simple to carry out. This representation is also used to characterize the types of marginal distributions that members of this class can have.
3 citations
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01 Oct 2010
TL;DR: In this article, a method for using a classical computer to generate a sequence of elementary operation (SEO) that can be used to operate a quantum computer, thereby inducing the quantum computer to sample an arbitrary probability distribution is described.
Abstract: We describe a method for using a classical computer to generate a sequence of elementary operation (SEO) that can be used to operate a quantum computer, thereby inducing the quantum computer to sample an arbitrary probability distribution. The probability distribution being sampled is specified in the form of a Bayesian network.
3 citations
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TL;DR: In this article, the exact first-order probability density function of the integrated speckle is calculated for a Gaussian scattering spot and circular detector aperture, compared with the corresponding approximate probability density functions, and the differences are discussed.
3 citations
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01 Aug 2016
TL;DR: In this paper, two new mixed Poisson distributions, namely, a three-parameter Poisson-exponentiated Weibull distribution and a fourparameter generalized Sichel distribution, were introduced to model over dispersed, zero-inflated, and long-tailed count data.
Abstract: A popular distribution for the modelling of discrete count data is the Poisson
distribution However, count data usually exhibit over dispersion or under dispersion
when modelled by a Poisson distribution in empirical modelling The presence of excess
zeros is also closely related to over dispersion Two new mixed Poisson distributions,
namely a three-parameter Poisson-exponentiated Weibull distribution and a fourparameter
generalized Sichel distribution is introduced to model over dispersed, zeroinflated
and long-tailed count data Some of the theoretical properties of the
distributions are derived and the distributions' characteristics are studied A Monte
Carlo simulation technique is examined and employed to overcome the computational
issues arising from the intractability of the probability mass function of some mixed
Poisson distributions For parameter estimation, the simulated annealing global
optimization routine and an EM-algorithm type approach for maximum likelihood
estimation are studied Examples are provided to compare the proposed distributions
with several other existing mixed Poisson models Another approach to modelling count
data is by examining the relationship between the counts of number of events which has
occurred up to a fixed time t and the inter-arrival times between the events in a renewal
process A family of count distributions, which is able to model under- and over
dispersion, is presented by considering the inverse Gaussian distribution, the
convolution of two gamma distributions and a finite mixture of exponential distributions
as the distribution of the inter-arrival times The probability function of the counts is
often complicated thus a method using numerical Laplace transform inversion for
computing the probabilities and the renewal function is proposed Parameter estimation
with maximum likelihood estimation is considered with applications of the count
distributions to under dispersed and over dispersed count data from the literature
3 citations