ProTest (version 2.4) [50] supported the use of JTT mixture model in this work (data not shown).
Given the generic mixture model (1), we choose to work with two tensors.
Fortunately, working with local mixture model can also resolve this artifact problem.
First we explain in Section 2.1 how we model the signal with a mixture of tensors and how our fiber model works.
The model works by computing a weighted Gaussian mixture model (GMM) response over the convex hull of each point set.
In this work, hidden Markov model and Gaussian mixture model (HMM-GMM) are employed, for both acoustic events and speech.
Bimodal genes were identified in the human genome by fitting a two-component mixture model, as detailed in the methods of our previous work [ 1].
The presented work shows that a Multivariate Gaussian Mixture Model as a framework for data-integration is suitable for the analysis and characterization of synthetic lethality data.
In addition to identifying modules in the genetic interaction network, another major contribution of our work is the use of a mixture model.
In this section, we introduce the generative mixture model of MixClone, which is an extension of our previous work on tumor purity estimation[ 11].
