Sentence examples for mixture model setting from inspiring English sources

Estimates of these latent quantities were obtained under a Bayesian mixture model setting.

Models and software for dealing with covariate non-response within a longitudinal mixture model setting are still in their infancy and previous work within the substance use literature has often adopted a relatively simplistic approach to these issues such as the use of a single imputed data set (Li et al., 2001; Hix-Small et al., 2004).

Expectation-maximization clustering was performed using hierarchical clustering for parameterized Gaussian mixture models, setting the number of clusters to 2 (one cluster of 'identified serotonergic neurons' and one of 'unidentified neurons'), with model selection by Bayesian Information Criterion.

For the grouping illustrated in Figure 2 the default cutoff for the posterior probability in the Gaussian Mixture Model was set to 0.5 to separate the small from the large group (Additional Files 3 and 4).

Input: : sequence of N data vectors K0: Number of initial Gaussian mixture components n: Sample size p: Number of components to fix at a time Output: : Parameters of the initial Gaussian mixture model (GMM) Obtain set of n random samples drawn from.

The employed Gaussian Mixture Models (GMMs) were set to have full covariance matrices for exploiting all possible correlations between the elements of each observation.

In order to facilitate comparisons to help determine the significance of temporal information contained in parameter contours as opposed to the statistical distributions of parameter values, another classification system based on Gaussian mixture models (GMMs) was set up.

Then we fit a beta-uniform mixture model on the entire set of raw P-values of all nodes in the interaction network.

We first fitted the mixture model with the complete set of 2500 species and a limited run length of 500 iterations.

Here we applied the cF mixture model to a large set of eukaryote phylogenomic data with 133 proteins from 40 taxa and 24294 sites [ 29] and calculated likelihoods of two competing trees: the LBA topology where Microsporidia group with Archaea and, the correct topology where Microsporidia group with Fungi.

Given a partition of the samples into a training and test set we applied the variational Bayesian Gaussian mixture model to learn from the training set the cluster means and variances for each tissue category.