Sentence examples for mixture models used from inspiring English sources

The implementation of Gaussian finite mixture models used here estimates a range of possible models using a combination of class configurations (See Figure 2 and Table 3 of Scrucca et al. [29]) and numbers of components/classes and selecting the combination that gives the highest BIC goodness of fit.

The mixture models used in this paper fit a pair of gaussian distributions to a vector.

Probability densities for normal mixture models used in our simulation studies are specified.

Our model is based on Gaussian mixture models used to describe the density of data representing sleep, implicitly containing Gaussian kernels corresponding to natural clusters in the data.

Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations.

This immediately implies the identifiability of parameters in many mixture models using a polynomial number of samples.

Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Eqns. Part I. Theory and Scheme.

This paper investigates semi-supervised learning of mixture models using a unified objective function taking both labeled and unlabeled data into account.

In step two, we fit Gaussian Mixture Models using EM-algorithm [17] as implemented in R/CNVtools [18] to each candidate CNV and record the relative distance between consecutive copy number classes.

Such connectivity profiles can be detected based on mixture models using software like MixNet [ 63].

21 Much literature is available on estimating mixture models using both frequentist and Bayesian paradigms.