Sentence examples for mixture models were from inspiring English sources

Most algorithms handling mixture models were originally specifically designed for processing mixtures of Gaussians.

Reinforced and unreinforced CMA mixture models were simulated in this research.

Combinations of four types of multispectral imagery ranging from 0.15 m resolution scanned color infrared aerial photography to 10 m resolution SPOT 5 multispectral imagery and three image processing models – per-pixel, object-based, and spectral mixture models – were tested.

As the number of data points that would be needed in order to obtain the necessary information for a complete description of the mixture would be prohibitive, mixture models were built using Principal Components Regression (PCR).

Gaussian mixture models were used to model each of the four classes.

Statistical mixture models were able to identify and purify major cell subsets in human peripheral blood, using an automated process that can be generalized to an arbitrary number of markers.

Mixture models are important modeling approaches that account for data heterogeneity.

So, mixture models are great, but they have a number of limitations.

Moreover, mixture models are an interesting and flexible model family.

Alright, the last extension I'm going to talk about for mixture models is multi-level category learning.

However, existing mixture models are constrained by assuming an known number of sub-populations.