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.
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.
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.
Using a simple mixture model where the bulk modulus of nanocrystals is the sum of the elastic responses from two structurally different components, the bulk modulus of the surface layer at 286 K is found to be 9.2% smaller than that of the crystalline grains, which is in qualitative agreement with the present finding.
This is in contrast with mixture models, where the mixture process is less intuitive.
