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 paper, the authors introduce the mixture of Kikuchi approximations as a probability model, which is tested in the approximation of probability distributions that arise in evolutionary computation, and they present an algorithm for learning Kikuchi approximation from data based on the expectationmaximization (EM) paradigm.
Abstract: Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.
14 citations
TL;DR: This paper explains how to combine probabilities when some or all of them are from discrete probability distributions, such as probability distributions for nonparametric tests.
Abstract: The combining of probabilities from separate studies has been discussed frequently in regard to probabilities based on continuous probability distributions. Little has been written, however, regarding the combining of probabilities from discrete distributions, such as probability distributions for nonparametric tests. This paper explains how to combine probabilities when some or all of them are from discrete probability distributions.
14 citations
TL;DR: In this paper, a new class of generalized two-sided power (TSP) distributions, where their density functions are expressed in terms of the Gauss hypergeometric functions, is introduced and studied.
Abstract: A new rich class of generalized two-sided power (TSP) distributions, where their density functions are expressed in terms of the Gauss hypergeometric functions, is introduced and studied. In this class, the symmetric distributions are supported by finite intervals and have normal shape densities. Our study on TSP distributions also leads us to a new class of discrete distributions on {0, 1, …, k}. In addition, a new numerical method for parameter estimation using moments is given.
14 citations
TL;DR: In this article, the negative Gibbs distributions arising in relativistic quantization are described by p-adic stochastic Stochastics, in the language of limits of relative frequencies, but with respect to ap-adic metric.
Abstract: A new mathematical apparatus, ap-adic theory of probability, is applied to realize the hypothetical world based on negative probability distributions created by Dirac for the relativistic quantization of photons. Within thep-adic theory of probability, negative probability distributions are well defined (in the language of limits of relative frequencies, but with respect to ap-adic metric). We propose that the negative Gibbs distributions arising in relativistic quantization are described byp-adic Stochastics.
14 citations
TL;DR: In this paper, Aly and Benkherouf presented a new family of distributions based on probability generating functions and derived a very useful representation for the Harris extended density function as an absolutely convergent power series of the survival function of the baseline distribution.
Abstract: A new method for generating new classes of distributions based on the probability-generating function is presented in Aly and Benkherouf [A new family of distributions based on probability generating functions. Sankhya B. 2011;73:70–80]. In particular, they focused their interest to the so-called Harris extended family of distributions. In this paper, we provide several general results regarding the Harris extended models such as the general behaviour of the failure rate function. We also derive a very useful representation for the Harris extended density function as an absolutely convergent power series of the survival function of the baseline distribution. Additionally, some stochastic order relations are established and limiting distributions of sample extremes are also considered for this model. These general results are illustrated in several special Harris extended models. Finally, we discuss estimation of the model parameters by the method of maximum likelihood and provide an application to real da...
14 citations