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In the univariate analysis, it is shown that a 3-component Lognormal mixture model provides a good fit to the marginal distribution function of fixation duration, and a hierarchical model is required for modeling saccade length.
The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance.
It was shown that the vMF mixture model provides a good fit to the angular estimates of the multidimensional parameter extraction algorithm, namely RiMAX [33].
A five class mixture model provides very close fit to the observed data at all levels of disease severity.
Since our mixture model provides an unsupervised AEI detection method, it is sensitive to the underlying parametric assumptions.
Our results indicate that the phylogenetic mixture model provides a general framework for measuring microsatellite conservation on a genomic scale.
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For the distribution of CpGO/E values, a two-component mixture model provided substantially better fit than a single component model [Additional file 1] and as a consequence, we modeled the distribution with a two-component model.
Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometric data.
A simulation study indicated that when the true model is specified, each of the proposed marginalized mixture models provides low biases, Type I errors and confidence interval coverages close to the nominal levels.
Section Moment Estimation for a Mixed-Poisson N-Mixture Model provides moment estimates and evaluates the use of two diagnostic tests for the negative-binomial case for when infinite estimates of abundance may arise.
Second, an unsupervised approach based on variational Bayesian Gaussian mixture modelling provided evidence for two clusters which mapped onto patients and controls with nearly the same accuracy (71%) as the supervised approach.
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