Sentence examples for mixture models using from inspiring English sources

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.

Buysse and colleagues (2004) tested linear and quadratic mixture models using separate models for the eszopiclone and placebo groups.

The first component consisted of the identification of the sub-network for each gene (i.e. the parent genes of each child gene), the estimation of the parameters of the mixture models using the Expectation-Maximization algorithm [ 13, 14], and the identification of the number of components of the mixture model best supported by the data.

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.

In this paper, we attempt to find the optimum designs for testing the presence of synergistic effects in a mixture model using the maximin criterion.

However, the algorithms of mixture model using expectation maximization (EM) technique are still too slow to deal with real million-node networks, since they compute hidden variables on the entire network in each iteration.