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nonGaussian density fields.
, 1996
"... A number of theories now set out to explain the initiation of density perturbations in the early universe. Since most of these ideas invoke new physics, with the prediction of additional fundamental fields, it is of interest to any cosmologist ..."
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A number of theories now set out to explain the initiation of density perturbations in the early universe. Since most of these ideas invoke new physics, with the prediction of additional fundamental fields, it is of interest to any cosmologist
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
CONDENSATION  conditional density propagation for visual tracking
, 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to th ..."
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Cited by 1503 (12 self)
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The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied
The minimax strategy for gaussian density estimation
 In COLT
, 2000
"... We consider online density estimation with a Gaussian of unit variance. In each trial t the learner predicts a mean θt. Then it receives an instance xt chosen by the adversary and incurs loss 1 2 (θt − xt) 2. The performance of the learner is measured by the regret de£ned as the total loss of the l ..."
Abstract

Cited by 10 (2 self)
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We consider online density estimation with a Gaussian of unit variance. In each trial t the learner predicts a mean θt. Then it receives an instance xt chosen by the adversary and incurs loss 1 2 (θt − xt) 2. The performance of the learner is measured by the regret de£ned as the total loss
WaveletBased Texture Retrieval Using Generalized Gaussian Density and KullbackLeibler Distance
 IEEE Trans. Image Processing
, 2002
"... We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step fo ..."
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Cited by 241 (4 self)
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distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods
Response histogram Gaussian density Probability
"... • Mixture of Gaussians, mixture of experts. • Hidden Markov models, linear Gaussian state space models. Models consisting of various combinations of: • Linear Gaussian, • mixture, • dynamical, See Roweis & Ghahramani (1999) A Unifying Review of Linear Gaussian Models. There is a need to go beyon ..."
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• Mixture of Gaussians, mixture of experts. • Hidden Markov models, linear Gaussian state space models. Models consisting of various combinations of: • Linear Gaussian, • mixture, • dynamical, See Roweis & Ghahramani (1999) A Unifying Review of Linear Gaussian Models. There is a need to go
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
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Cited by 661 (23 self)
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. In Proc. European Conf. Computer Vision, 1996, pp. 343356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent
Response histogram Gaussian density Probability
"... • Factor analysis, principle components analysis, Probabilistic PCA. ..."
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 693 (4 self)
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We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2
Results 1  10
of
5,333