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More on Multivariate Gaussians
, 2008
"... Up to this point in class, you have seen multivariate Gaussians arise in a number of applications, such as the probabilistic interpretation of linear regression, Gaussian discriminant analysis, mixture of Gaussians clustering, and most recently, factor analysis. In these lecture notes, we attempt to ..."
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Up to this point in class, you have seen multivariate Gaussians arise in a number of applications, such as the probabilistic interpretation of linear regression, Gaussian discriminant analysis, mixture of Gaussians clustering, and most recently, factor analysis. In these lecture notes, we attempt
Multiplierless Algorithm for Multivariate Gaussian . . .
- IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
, 2012
"... The multivariate Gaussian distribution is used to model random processes with distinct pair-wise correlations, such as stock prices that tend to rise and fall together. Multivariate Gaussian vectors with length n are usually produced by first generating a vector of n independent Gaussian samples, ..."
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Cited by 1 (0 self)
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The multivariate Gaussian distribution is used to model random processes with distinct pair-wise correlations, such as stock prices that tend to rise and fall together. Multivariate Gaussian vectors with length n are usually produced by first generating a vector of n independent Gaussian samples
Differential Entropic Clustering of Multivariate Gaussians
- Adv. in Neural Inf. Proc. Sys. (NIPS
, 2006
"... Gaussian data is pervasive and many learning algorithms (e.g., k-means) model their inputs as a single sample drawn from a multivariate Gaussian. However, in many real-life settings, each input object is best described by multiple samples drawn from a multivariate Gaussian. Such data can arise, for ..."
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Cited by 36 (3 self)
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Gaussian data is pervasive and many learning algorithms (e.g., k-means) model their inputs as a single sample drawn from a multivariate Gaussian. However, in many real-life settings, each input object is best described by multiple samples drawn from a multivariate Gaussian. Such data can arise
AN ENTROPIC PATHWAY TO MULTIVARIATE GAUSSIAN DENSITY
, 709
"... A general principle called “conservation of the ellipsoid of concentration ” is introduced and a generalized entropic form of order α is optimized under this principle. It is shown that this can produce a density which can act as a pathway to multivariate Gaussian density. The resulting entropic pat ..."
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A general principle called “conservation of the ellipsoid of concentration ” is introduced and a generalized entropic form of order α is optimized under this principle. It is shown that this can produce a density which can act as a pathway to multivariate Gaussian density. The resulting entropic
Remarks on multivariate Gaussian Gabor frames
, 2010
"... We report on initial findings on Gabor systems with multivariate Gaus-sian window. Unlike the existing characterisation for dimension one in terms of lattice density, our results indicate that the behavior of Gaussians in higher-dimensional Gabor systems is intricate and further exploration is a val ..."
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We report on initial findings on Gabor systems with multivariate Gaus-sian window. Unlike the existing characterisation for dimension one in terms of lattice density, our results indicate that the behavior of Gaussians in higher-dimensional Gabor systems is intricate and further exploration is a
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1-norm penalty term. The problem as formulated is convex but the memor ..."
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Cited by 334 (2 self)
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We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1-norm penalty term. The problem as formulated is convex
ON THE UNIMODALITY AND THE BIMODALITY OF THE MULTIVARIATE GAUSSIAN MIXTURES OF THE TWO COMPONENTS
"... In the artical several sufficient conditions of the unimodality and necessary condition of the bimodality are formulated for the multivariate Gaussian mixtures of two components with the equal covariance matrixes Σ and with various vectors of expectation values �i, i = 1, 2. The results are received ..."
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In the artical several sufficient conditions of the unimodality and necessary condition of the bimodality are formulated for the multivariate Gaussian mixtures of two components with the equal covariance matrixes Σ and with various vectors of expectation values �i, i = 1, 2. The results
Multivariate Gaussian simulation outside arbitrary ellipsoids
- Journal of Computational and Graphical Statistics
"... Methods for simulation from multivariate Gaussian distributions restricted to be from outside an arbitrary ellipsoidal region are often needed in applications. A standard rejection algorithm that draws a sample from a multivariate Gaussian distribution and accepts it if it is outside the ellipsoid i ..."
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Cited by 1 (0 self)
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Methods for simulation from multivariate Gaussian distributions restricted to be from outside an arbitrary ellipsoidal region are often needed in applications. A standard rejection algorithm that draws a sample from a multivariate Gaussian distribution and accepts it if it is outside the ellipsoid
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