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

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

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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

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

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

• 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

. 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

• Factor analysis, principle components analysis, Probabilistic PCA.

We describe the maximum-likelihood parameter estimation problem and how the Expectation-form 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