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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Papers
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14 Feb 2013TL;DR: In this paper, Monte Carlo simulation (MCS)-based procedures for modeling the joint probability disambiguation of multivariate distributions have been proposed for multivariate distribution simulation, which has not been extensively investigated.
Abstract: The simulation of multivariate distributions has not been investigated extensively. This article aims to propose Monte Carlo simulation (MCS)-based procedures for modeling the joint probability dis...
13 citations
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03 Apr 2008TL;DR: Textured sonar imagery is modeled by a correlated K-distribution via the compound representation of the one-dimensional K-Distribution probability density function and model parameters are estimated from a set of texturedSonar images using a nonlinear least-squares fit algorithm.
Abstract: Single-point statistical properties of envelope-detected data such as signal returns from synthetic aperture radar and sonar have traditionally been modeled via the Rayleigh distribution and more recently by the K-distribution. Two-dimensional correlations that occur in textured non-Gaussian imagery are more difficult to model and estimate than Gaussian textures due to the nonlinear transformations of the time series data that occur during envelope detection. In this research, textured sonar imagery is modeled by a correlated K-distribution. The correlated K-distribution is explained via the compound representation of the one-dimensional K-distribution probability density function. After demonstrating the model utility using synthetically generated imagery, model parameters are estimated from a set of textured sonar images using a nonlinear least-squares fit algorithm. Results are discussed with regard to texture segmentation applications.
13 citations
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TL;DR: It is shown that by treating the cumulants as elements of the polynomial ring the authors can directly solve the unsupervised learning problem of finding the subspace on which several probability distributions agree, at a lower computational cost and with higher accuracy.
Abstract: We propose a novel algebraic algorithmic framework for dealing with probability distributions represented by their cumulants such as the mean and covariance matrix. As an example, we consider the unsupervised learning problem of finding the subspace on which several probability distributions agree. Instead of minimizing an objective function involving the estimated cumulants, we show that by treating the cumulants as elements of the polynomial ring we can directly solve the problem, at a lower computational cost and with higher accuracy. Moreover, the algebraic viewpoint on probability distributions allows us to invoke the theory of algebraic geometry, which we demonstrate in a compact proof for an identifiability criterion.
13 citations
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TL;DR: This paper attempts to interpret the standard statistical distributions in the innovation diffusion context by classifying a statistical distribution as being an internal, an external, or a mixed influence model.
13 citations
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TL;DR: In this paper, the authors construct goodness-of-fit tests for continuous distributions using their characterizations in terms of moments of order statistics and moments of record values, based on characterizations presented in [2] and [3].
Abstract: We construct goodness-of-fit tests for continuous distributions using their characterizations in terms of moments of order statistics and moments of record values. Our approach is based on characterizations presented in [2]–[4], [5], [9].
13 citations