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Multivariate normal mixture model clustering results of the Tamba area.

(a) Calculate a spatial density map of the previous earthquakes in the area using a multivariate normal mixture model.

Tarabalka and Haavardshlom [2] have implemented the anomaly detection in hyperspectral image using multivariate normal mixture models on GPU.

Then, this information is converted into a forecast via an autoregressive process and a multivariate normal mixture model (Everitt, 1993; Chatfield, 2004).

These predictions are overlaid on a spatial density map obtained with a multivariate normal mixture model of the historical earthquakes that have occurred in the area.

These predictions are superimposed on a spatial density map obtained with a multivariate normal mixture model for historical earthquakes that occurred in the area.

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Ward's method joins clusters to maximize the likelihood at each level of the hierarchy under the assumptions of multivariate normal mixtures, spherical covariance matrices, and equal sampling probabilities.

The most complex models (4 and 5) were multivariate normal-binary mixture models including growth, sexual maturity and field survival traits.

Among these methods are those using finite mixture densities with a range of approaches including mixtures for multivariate normal distributions as well as mixtures for categorical data (latent class analysis) and Bayesian analysis of mixtures.

The multivariate p.d.f of load cycles is modelled with a mixture of multivariate normal functions.

The BFR algorithm is based on the assumption that the clusters' distribution results from a mixture of multivariate normal distributions, whose parameters are estimated from the data streams.

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