MVN

(redirected from Multivariate Normal)
AcronymDefinition
MVNMultivariate Normal (probability theory)
MVNMultivariate Normal Distribution (statistics)
MVNMalvern (Amtrak station code; Malvern, AR)
MVNMost Valuable Network (sports-related web site)
MVNMobile Virtual Network
MVNMount Vernon Net
MVNMission Viejo Nadadores (swimming group)
MVNMount Vernon, IL, USA - Mount Vernon Outland Airport (Airport Code)
MVNMohawk Valley Network (New York)
MVNMark L. Van Name (science fiction author)
Copyright 1988-2018 AcronymFinder.com, All rights reserved.
References in periodicals archive ?
With the hypothesis that all variables follow a multivariate normal distribution, this approach is based on the multivariate normal (MVN) model to determine completion values.
In this article, we propose a mixture of three-component multivariate normal distributions to fit the expression levels of miRNAs to identify DV miRNAs between cases and controls.
where [[beta].sub.0] is a vector of length m, [[beta].sub.X] is an m x p matrix, [[beta].sub.1] is a vector of length m, and the random error [epsilon] is of length m and is assumed to follow a zero mean multivariate normal distribution with covariance [SIGMA], [epsilon] ~ N(0,[summation]).
Formulating the prior information about all even and odd amplitudes as a multivariate normal with a specific prior mean and covariance gives straightforwardly the posterior mean and covariance.
From Table 1, both the standard deviation values for A02 and A03 passed the normality and independence tests, indicating that standard deviation values of the residual are independent and obey the multivariate normal distribution, which is consistent with the assumption that the observation of the HSMM described in Section 3 obeys multivariate normal distribution.
Natural choices of [pi](*) would be the multivariate normal or Student t densities with posterior sampling mean and covariance.
Linear models with multivariate normal distributions have the unique advantage that causality and partial correlations are directly linked, largely simplifying the computation of transfer entropy, and directly mapping the problem into the sparse inverse covariance problem [3, 4].
To investigate the performance of our proposed ([beta]-NBC) classifier in a comparison with four popular classifiers (KNN, NBC, SVM, and AdaBoost), we generated both training and test datasets from m = 2 multivariate normal distributions with different mean vectors ([[mu].sub.k], k = 1, 2) of length p =10 but common covariance matrix ([[LAMBDA].sub.k] = [LAMBDA]; k = 1,2).
The goal of the Bartlett's sphere test is to determine whether the required data is fetched from the general of the multivariate normal distribution or not.
Full browser ?