Sentence examples for mixture model variance from inspiring English sources

Finally a test statistic (e.g. F or likelihood ratio test), based on a given model (e.g. regression, mixture model, variance component model...) is performed at each putative QTL position on the chromosomal segments traced.

We find VarMixt more subtle: it makes the assumption that classes of genes can be identified based on similar response to the various sources of variability (mixture model); the variance of each homogeneous class is then accurately estimated from a large set of observations; the individual gene variance is then replaced by its "class" variance.

Presumably it benefits from a more realistic mixture model on variances, less likely to generate false-positives.

A K-components Gaussian mixture model with equal variances was chosen to modelize the marginal distribution of the transformed test statistics, as such a mixture model efficiently separates the empirical null distribution (likely to be composite and different from the theoretical one [28], [29]) from the alternative distribution.

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures.

Simple pooled variances of such genes are likely to be larger than that of a single density of the mixture model because the pooled variance is equivalent to the sum of the variances from each component of the mixture model.

The Gaussian mixture model improves the prior variance estimation from GEO database in terms of performance exploiting the multi-distributional structure in GEO database.

At the same time, the choice of Ls, as noted above, helps to maintain the assumption that all Gaussian distributions in a mixture model will have similar variances.

Assuming various sources of variance, we estimated the gene variance using a mixture model integrated into the VarMixt method [ 78].

GMixBayes is modified from earlier GEO-adjusted methods in two aspects: estimation of prior variance using Gaussian mixture model and Bayesian integration of both testing and reference dataset variances.

LCGA is a type of growth mixture modelling where the variance and covariance estimates for growth factors within each class are fixed to zero, assuming homogeneity within classes.