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

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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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

We consider on-line 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

. In Proc. European Conf. Computer Vision, 1996, pp. 343--356, 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

• 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

We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate

• Factor analysis, principle components analysis, Probabilistic PCA.

This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray manifold, beamforming is made robust with respect to array deformations, distortion of the wave front, pointing errors, etc ... so that neither array calibration nor physical modeling are necessary. Rather surprisingly, `blind beamformers' may outperform `informed beamformers' in a plausible range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption blind identification relies on is the statistical independence of the sources, which we exploit using fourth-order cumulants. A computationally efficient technique is presented for the blind estimation of directional vectors, based on joint diagonalization of 4th-order cumulant matrices