Results 211 to 220 of about 21,121 (253)

Optimal Quantization of the Rayleigh Probability Distribution

IEEE Transactions on Communications, 1979
We have derived an efficient and general algorithm for optimal quantization and applied it to the important, but neglected Rayleigh random variable. The optimal quantization range and output levels for the distortion criterion of minimum mean squared error have been calculated and tabulated for numbers of output levels from 1 to 64. We also compute and
William A. Pearlman, George H. Senge
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On multivariate rayleigh and exponential distributions

IEEE Transactions on Information Theory, 2003
Summary: Expressions for multivariate Rayleigh and exponential probability density functions (PDFs) generated from correlated Gaussian random variables are presented. We first obtain a general integral form of the PDFs, and then study the case when the complex Gaussian generating vector is circular.
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Stability of the Rayleigh distribution

2011 4th International Congress on Image and Signal Processing, 2011
Recent neurophysiological studies have evaluated neural synchronization using wavelet transformation and the phase-locking factor (PLF). However, the practical stability of the distribution was not efficiently discussed. Mathematization of PLF was performed, and results for PLF simulation were obtained, using a program for retrieving randomized samples
Takefumi Ueno, Shogo Hirano, Yoji Hirano, Naoya Oribe, Itta Nakamura, Yuko Oda, Shigenobu Kanba, Toshiaki Onitsuka   +7 more
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Maxwellian distribution versus Rayleigh distribution

American Journal of Physics, 1986
The inverse problem of deducing the molecular velocity distribution from a given density function is solved analytically. The results corresponding to two physically interesting systems viz. Maxwellian and Rayleigh gases are compared.
V. J. Menon, D. C. Agrawal
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A New Bivariate Distribution with Rayleigh and Lindley Distributions as Marginals

Journal of Statistical Theory and Practice, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thomas, P. Yageen, Jose, Jitto
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Methodology-specific rayleigh-match distributions

Vision Research, 1992
Abstract Direct comparison of Rayleigh-match midpoint distributions obtained with different psychophysical methods reveals that unimodality is associated with the method-of-adjustment and multimodality is associated with the forced-choice method.
T P, Piantanida, J, Gille
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Weighted Rayleigh Distribution

International Journal of Research and Innovation in Applied Science
This paper introduces the Weighted Rayleigh (WR) distribution by inducing inverted weight function into the existing Rayleigh distribution. Statistical and mathematical expressions of its properties such as Survival Function, Hazard Function, Moments, Moment Generating Function, Mean Deviation and Renyi entropy were explicitly derived.
Adetunji K. Ilori, Omaku P. Enesi, Kole Emmanuel, Dayo V. Kayode, Adebisi Michael   +4 more
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Modelling SAR images with a generalisation of the Rayleigh distribution

Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154), 2002
Synthetic aperture radar (SAR) imagery has found important applications due to its clear advantages over optical satellite imagery one of them being able to operate in various weather conditions. However, due to the physics of the radar imaging process, SAR images contain unwanted artifacts in the form of a granular look which is called speckle.
Kuruoglu EE, Zerubia J
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Exponentiated Strongly Rayleigh Distributions.

2018
© 2018 Curran Associates Inc..All rights reserved. Strongly Rayleigh (SR) measures are discrete probability distributions over the subsets of a ground set. They enjoy strong negative dependence properties, as a result of which they assign higher probability to subsets of diverse elements. We introduce in this paper Exponentiated Strongly Rayleigh (ESR)
Mariet, Zelda, Sra, Suvrit, Jegelka, Stefanie Sabrina   +2 more
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HPD Prediction Intervals for Rayleigh Distribution

IEEE Transactions on Reliability, 1985
Using Jeffrey's non-informative prior, the predictive pdf of a future observation and that of the k-th order statistic of a future sample from a Rayleigh distribution have been obtained. Bayes predictive estimators and highest posterior density prediction intervals for the future observation and the k-th order statistic are derived. A numerical example
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