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 new system of multivariate distributions with fixed marginal distributions is introduced via the consideration of random variates that are randomly chosen pairs of order statistics of the marginal distributions.
49 citations
TL;DR: In this article, the authors evaluated the degree to which the visibility of these fringes is degraded by a homodyne detector with less than unit quantum efficiency and proposed an experiment employing conjugate pairs of photons generated via a parametric down conversion or four-wave mixing.
Abstract: A homodyne detector measures a field-amplitude component of the incoming signal. If the incoming signal is in an $n$-photon eigenstate the homodyne detector's output probability distribution exhibits $n$ fringes. Here the degree to which the visibility of these fringes is degraded by a homodyne detector with less than unit quantum efficiency is evaluated. An experiment employing conjugate pairs of photons generated via a parametric down conversion or four-wave mixing is proposed by which these fringes could be observed.
48 citations
48 citations
TL;DR: The algorithm proposed here has an acceptance probability which is superior to e/4 and the efficiency of the algorithm is compared with the previous method and the improvement in terms of minimum acceptance probability is shown.
Abstract: We study the properties of truncated gamma distributions and we derive simulation algorithms which dominate the standard algorithms for these distributions. For the right truncated gamma distribution, an optimal accept–reject algorithm is based on the fact that its density can be expressed as an infinite mixture of beta distribution. For integer values of the parameters, the density of the left truncated distributions can be rewritten as a mixture which can be easily generated. We give an optimal accept–reject algorithm for the other values of the parameter. We compare the efficiency of our algorithm with the previous method and show the improvement in terms of minimum acceptance probability. The algorithm proposed here has an acceptance probability which is superior to e/4.
47 citations