Sentence examples for mixture model framework from inspiring English sources

Based on parametric and non-parametric mixture model framework, this paper classifies spectrum occupancy modelling approaches in literature based on predictor class selection.

We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework.

Model classification presented in this section is displayed under mixture model framework.

Then, based on the stochastic mixture model framework, we review parametric and non-parametric approaches for underlying stochastic source assignment.

More precisely, linear discriminant analysis in conjunction with acoustic model adaptation, subspace Gaussian mixture model framework, discriminative training, and additional dithering was described.

By incorporating the allelic configuration of an iQTL genotype into a mixture model framework, this strategy provides a path to trace the parental origin of alleles from previous generations.

Therefore, we see the beta-mixture model framework of BMIQ as an important conceptual advantage over SQN/SWAN, since, as demonstrated here, it successfully normalises type2 probe values, faithfully preserving the numerous and complex biological differences that exist between the two designs without ever needing to define probe subsets.

Here we build on these previous methods by introducing two mechanistic approaches (Dirichlet mixture and CLR transform) to modeling individual variability within a hierarchical Bayesian mixing model framework.

An empirical Bayes approach in the mixture-model framework was developed to assess differential gene expression data from Affymetrix platform.

The model incorporates quantitative genetic principles into a mixture-model framework in which various genetic effects, including additive, dominant, imprinting, and their interactions, are estimated by implementing the EM algorithm.

Aickin [ 15] proposed a model-based estimate using maximum likelihood estimation for estimating alpha, while given the categorical latent variable, Aickin's model can be shown to be a log-linear model within a mixture-model framework [ 22].