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The linear mixture model assumes single scattering between the endmember substances resulting from the fact that they are sitting side-by-side within the field of view of the imaging instrument (see Figure 2a).
The mixture model assumes that each observation comes from one of an assumed set of distributions.
The mixture model assumes several components with different sets of branch lengths.
Unlike the Bayes B method [ 2], this mixture model assumes that SNP marker effects are sampled from a single (normal) distribution, instead of estimating marker-specific variances.
The 3M Markov mixture model assumes that pathway sequences can be represented with a mixture of first order Markov chains [ 9].
Recursive-partitioning mixture model assumes a β-mixture model to split samples between subgroups and provides an estimate for the number of clusters; furthermore, it is computationally efficient relative to the standard finite mixture model approach (Houseman et al, 2008).
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Thus, we can form a mixture model, assuming for all : (8).
Rather than trying to estimate an event effect, which makes no sense since these cannot be estimated, we suggest to use a mixture model assuming that a priori the horse population is a mixture.
To decompose the distribution into its two components we used a simple mixture model: Assume that the distributions of scores of "ancestral alternative" and "other" exons are represented by the functions f a (x) and f o (x) respectively.
In this study, the mixture model assumed that the observed birth weights came from a population that consisted of two components corresponding to the appropriate for gestational age and large for gestational age subgroups.
In general, mixture models assume that the population consists of subgroups, for which the model parameters differ in particular ways (Mislevy and Verhelst 1990; Rost 1990).
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