The standardized distributions for DBH and HT are shown in Fig. 3. Species are then assigned to the resulting standardized DBH-HT pairs using a finite mixture distribution (Zhang et al. 2001; Liu et al. 2002) based on relative DBHs of the species mixtures.
We use a finite mixture model in this study.
Each group can be modeled using a finite mixture model or a DP.
Ghosh and Chinnaiyan [ 2] proposed a mixture model-based approach to classify genes based on the expression of independent samples corresponding to melanoma or prostate cancer diagnoses using a finite mixture of multivariate Normal distributions.
Accordingly, we first categorize the comparable SNPs based on the sequencing coverage counts (rescaled after library size adjustments) using a finite mixture of univariate Poisson distributions, and subsequently search for AEI signals within each group of comparable SNPs by fitting a folded Skellam mixture model to the absolute values of rescaled read differences.
In one such example, however, Siegmund et al. (2006) used a finite mixture model to estimate the association between exposure and latent disease subtype measured by DNA methylation profiles and compared these results with a simpler two-phase approach, first clustering the DNA methylation data and then relating these clusters to exposure using logistic regression.
Taslim et al. developed DIME that takes normalized differences of ChIP-seq counts in each genomic bin as input and used a finite mixture model (non-differential, negative/positive differential) to identify genes with differential binding for a specific protein in two conditions [ 18, 19].
Pyne et al. [ 24] use a finite mixture of skew and heavy-tailed multivariate distributions fitted with an EM algorithm and validated the approach on lymphoblastic cell lines and regulatory T cells.
For the remainder of the samples, copy number calling was done jointly on KIR3DL1 and KIR3DS1 using unsupervised clustering with a finite mixture model.
Using a functional data analysis approach, disturbance patterns metrics were modelled as curves and scores from a functional principal components analysis were clustered using a Gaussian finite mixture model.
GRAMMy formulates the problem as a finite mixture model, using the Expectation-Maximization (EM) algorithm to estimate the relative genome abundances.
