Sentence examples for mixture models where from inspiring English sources

So for example, in Bayesian estimation, people often use it for mixture models where you actually have a mixture of multiple different distributions.

Unsupervised data clustering can be addressed by the estimation of mixture models, where the mixture components are associated to clusters in data space.

The k-means algorithm can be thought as a simplified version of Gaussian mixture models, where the weights and the variances of the classes are assumed to be equal.

This is in contrast with mixture models, where the mixture process is less intuitive.

If the covariate space is determined in a data-driven way, different mixture models where the flexibility of the spline functions is varied need to be compared using model selection techniques.

This seems to be an example of a so-called 'indirect interpretation of mixture models' where classes do not represent different types of subjects, but rather approximate different parts of the joint distribution of observed data.

Experimental data have been compared to numerical simulations obtained using both a two-fluid model and a single-fluid mixture model (where only the density difference is accounted for).

A related notion of local FDR (ℓFDR) can be appropriately represented by considering that the observed p-values come from a two-component mixture model where the component corresponding to the null hypothesis is known.

The example treated in this paper involves mixture modeling where blocks of data are available on the final product properties (Y), on the process conditions used to manufacture the products (Z), on the ratios of raw materials used in the formulations (R) and on the properties of each of those raw materials (X).

In contrast to traditional methods, our approach addresses the multi-target tracking problem using the M-component non-parametric mixture model, where each component (every landmark point) is modeled with an individual particle filter that forms part of the mixture.

Our suggested analytical expression of the BER, based on the Gaussian Mixture model, where parameters are jointly estimated by EM algorithm and Mutual Information theory leads to an efficient fast way to estimate the performance of a digital communications system.