Sentence examples for mixture modeling is from inspiring English sources

Growth mixture modeling is an individual-based modeling technique that permits investigators to explore the longitudinal features of disease progression (i.e., symptom trajectories) and to cluster patients into latent classes (subgroups) based on the differential symptom courses [ 32].

For the same reasons, in this paper, the concept of mixture modeling is adopted for the characterization of the spatial correlation function.

The S score versus posterior probability curve in the phosphorylation EMAP dataset is shown in Figure 3, and the fitting of mixture model on the same dataset is shown in Figure 4. Our mixture modeling is a soft threshold technique.

If the mixture modeling is effective, we expect { q1,..., q10} (corresponding to P) to be significantly different from { q11,..., q20} (corresponding to W), with one being high, and the other being low.

Maximum likelihood estimation of mixture modeling is nontrivial and time consuming; it requires an iterative algorithm because mixture models generally exhibit multiple local maxima of the likelihood, and so fitting procedures must be based on many starting values to ensure that a global maximum is found (32).

Mixture modeling was implemented according to [ 2].

Gaussian mixture modeling was well established for CN calling from large data sets.

Multivariate Gaussian Mixture Modeling was applied to divide different subsets of a heterogeneous genomic data set into two groups.

To handle complex distribution, a mixture model is more appropriate.

A mixture model is more appropriate tool to handle the multimodal distribution.

samples,, the log-likelihood function for the Gaussian mixture model is expressed as follows: (3).