Sentence examples for mixture model approach from inspiring English sources

A mixture model approach for the analysis of microarray gene expression data.

We use a mixture model approach to allow for different types of individual preferences.

The collapse of granular material submerged in a water is then investigated using a Mixture Model approach.

The simple Mixture Model approach gave reasonably good predictions of the Rondon et al. (2011) experiments for the case of initially loose piles that collapsed in about a second, but it was unsuccessful in simulating the collapse of the initially dense piles that were observed by Rondon et al. (2011) to take around 30 40 s.

We benchmarked our method against 2 commonly used analysis strategies, not including the Gaussian mixture model approach [24] [27], which produced uninterpretable results for our experiments (Figure 2).

Once average functional statistical Ζ-maps (thresholded at a corresponding p<0.05) were created by a generalized mixture model approach [13], for this particular study they were co-registered onto the reconstruct average surface brains of the subjects using Freesurfer.

They also suggest competing advantages for the regression tree and Bayesian mixture model approaches, making both reasonable default engines for multiple imputation of categorical data.

In the circumstances of this study, the results suggest that default chained equations approaches based on generalized linear models are dominated by the default regression tree and Bayesian mixture model approaches.

In particular, the Gaussian mixture model approaches that were previously used in proteomics applications were not robust to experiments with non-Gaussian tails, whereas the density estimation methods developed for gene expression analysis tended to over-fit regions of data sparsity.

Mixture model approaches have been proposed to overcome some of the limitations of clustering.

Consequently, a large proportion of samples were misclassified when we attempted unsupervised clustering using bivariate finite mixture model approaches, first with PlatinumCNV [ 18], then with our own, mixture of beta-Gaussian distributions, approach.