Sentence examples for by a mixture model from inspiring English sources

RMA background correction estimates the background by a mixture model, whereby the background signals are assumed to be normally distributed and the true signals are exponentially distributed.

The idea underlying the MIX model is to replace a continuous model for θ i by a mixture model that uses a variable number of risk classes and a spatially correlated allocation model to distribute each area to a class.

We next simulated genetic heterogeneity (Table 3B) with 0.5/0.5, 0.7/0.3 proportions of subjects affected by a mixture model of epistasis from loci 1x2 and loci 4*5.

The p-values from multiple hypothesis testing, for n hypotheses, can be described by a mixture model g(p) (1) with two components: one component g0(p) originates from true null hypotheses and follows uniform distribution U 0, 1) [ 4], and the other component g1(p) results from true alternative hypotheses and follows a distribution confined to the p-values close to 0 [ 5].

We achieve this by fitting a mixture model with S categories, considering all potential species simultaneously.

The probabilistic segmentation was performed by fitting a mixture model of 2 Gaussians to the normalized histogram within the masked GTV using the EM algorithm.

Yet, this model remains homogeneous over positions, a constraint that could be released by considering a mixture model in which the parameters of the covarion process are component specific.

Grzegorczyk et al. [ 9] proposed a method to learn a non-homogeneous Bayesian network that can represent multiple different conditions, by using a mixture model that unifies networks of different conditions.

For each array, background correction was performed by fitting a mixture model of a normal and exponential distribution where normal distribution captures the non-expressed probes and exponential distribution the expressed probes [ 20].

ProteinProphet is used to address the protein inference problem by applying a mixture model based on the number of distinct peptides per protein (sibling peptides) to boost the probabilities of peptides with multiple siblings, while penalizing peptides without siblings.

We infer the chromatin state by using a mixture model for the DNase HS data: the open state should be associated with higher tag densities from the DNase data, and the closed state with lower densities.