Results 31 to 40 of about 366,433 (291)

The Sampling Distribution of the Total Correlation for Multivariate Gaussian Random Variables

Entropy, 2019
The sampling distribution of the total correlation (TC) for a d-dimensional standardized multivariate Gaussian random variable with an identity covariance matrix is derived. It is shown to be the distribution of a sum of generalized beta random variables.
Taylor Rowe, Troy Day
doaj   +1 more source

An RMT-based generalized Bayesian information criterion for signal enumeration

EURASIP Journal on Advances in Signal Processing, 2023
This paper provides a method for enumerating signals impinging on an array of sensors based on the generalized Bayesian information criterion (GBIC). The proposed method motivates by a statistic for testing the sphericity of the covariance matrix when ...
Shoucheng Yuan, Bin Zhang
doaj   +1 more source

Gaussian Distributed Noise Generator Design Using MCU-STM32

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022
The random noise signal is widely used as a test signal to identify a physical or biological system. In particular, the Gaussian distributed white noise signal (Gaussian White Noise) is popularly used to simulate environmental noise in telecommunications system testing, input noise in testing ADC (Analog to Digital Converter) devices as well as testing
M. Nanak Zakaria, Setiawan, Achmad, Ahmad Wilda Y, Lis Diana Mustafa   +3 more
openaire   +3 more sources

Multiplying a Gaussian Matrix by a Gaussian Vector

, 2017
We provide a new and simple characterization of the multivariate generalized Laplace distribution. In particular, this result implies that the product of a Gaussian matrix with independent and identically distributed columns by an independent isotropic ...
Mattei, Pierre-Alexandre
core   +1 more source

A Bivariate Extension of Type-II Generalized Crack Distribution for Modeling Heavy-Tailed Losses

Mathematics
As an extension of the (univariate) Birnbaum–Saunders distribution, the Type-II generalized crack (GCR2) distribution, built on an appropriate base density, provides a sufficient level of flexibility to fit various distributional shapes, including heavy ...
Taehan Bae, Hanson Quarshie
doaj   +1 more source

Generalized independent low-rank matrix analysis using heavy-tailed distributions for blind source separation

EURASIP Journal on Advances in Signal Processing, 2018
In this paper, statistical-model generalizations of independent low-rank matrix analysis (ILRMA) are proposed for achieving high-quality blind source separation (BSS). BSS is a crucial problem in realizing many audio applications, where the audio sources
Daichi Kitamura, Shinichi Mogami, Yoshiki Mitsui, Norihiro Takamune, Hiroshi Saruwatari, Nobutaka Ono, Yu Takahashi, Kazunobu Kondo   +7 more
doaj   +1 more source

The Use of Generalized Gaussian Distribution in Vibroacoustic Detection of Power Transformer Core Damage

Energies, 2020
Vibroacoustic diagnostics (VM—Vibroacoustic Method) is one of the methods for diagnosing the active part of power transformers. Measurement technologies have been refined over the past several years, but the methods of analyzing data obtained in VM ...
Robert Krupiński, Eugeniusz Kornatowski
doaj   +1 more source

State Estimation Based on Generalized Gaussian Distributions

Metrology and Measurement Systems, 2013
Abstract This paper presents a novel strategy of particle filtering for state estimation based on Generalized Gaussian distributions (GGDs). The proposed strategy is implemented with the Gaussian particle pilter (GPF), which has been proved to be a powerful approach for state estimation of nonlinear systems with high accuracy and low computational
Xifeng Li, Yongle Xie
openaire   +1 more source

Multivariate Generalized Gaussian Distribution: Convexity and Graphical Models [PDF]

IEEE Transactions on Signal Processing, 2013
We consider covariance estimation in the multivariate generalized Gaussian distribution (MGGD) and elliptically symmetric (ES) distribution. The maximum likelihood optimization associated with this problem is non-convex, yet it has been proved that its global solution can be often computed via simple fixed point iterations.
Zhang, Teng, Wiesel, Ami, Grec, Maria Sabrina   +2 more
openaire   +2 more sources

ML: Generalized Additive Model for Spectrum Sensing in Nakagami-m Fading Channel With Complex Generalized Gaussian Distribution Noise

IEEE Access
Spectrum sensing in cognitive radio presents a challenge in overcoming the spectrum scarcity caused by the rapid growth of wireless devices. Deep learning (DL) and machine learning (ML) are trending approaches to enhance the spectrum sensing performance.
Md. Tofail Ahmed, Mousumi Haque, Yosuke Sugiura, Tetsuya Shimamura   +3 more
doaj   +1 more source