This modification takes into account the interaction between binary mixture components and an adsorbent.
HPTLC offers a great variety of stationary phases with unique selectivity for mixture components and their separation simultaneously.
A comprehensive database is collected from the literature to cover a wide range of mixture components and mix proportions.
The success of the model depends on its ability to understand the correlation between the mixture components and the measured engineering properties.
Influence of initial setting of parameters (number of mixture components and used number of iterations) for GMM training process was analyzed, too.
Additionally, in these evaluations, there is not a one-to-one correspondence between the number of Gaussian mixture components and the number of sources.
where h* i) is the index of the part that matches feature i of the observed object, K h * i s is the number of mixture components, α h * i, m s is the weight of the m th mixture component, and μ h * i, m s and Σ h * i, m s are the mean vector and covariance matrix of the m th Gaussian component, respectively.
where h* i) is the index of the part that matches feature i of the observed object, K h * i p is the number of mixture components, α h * i, m p is the weight of the m th mixture component, and μ h * i, m p and Σ h * i, m p are the mean vector and covariance matrix of the m th Gaussian component, respectively.
The first six samples collected between 0 and 150 minutes were assigned to one mixture component and six samples obtained from 240 to 390 minutes were assigned to the other mixture component.
Fitting of the folded Skellam mixture model only used the 10702 SNPs in 3' UTR We fit the folded Skellam mixture model to the adjusted read pairs classified into the first Poisson mixture component, and only use SNPs on the 3' UTR.
For Σ DTree, we randomly generate tree structures, one for each mixture component, and then choose from [0.01,1.0] and w u | v, k from [0.0,0.5] for Σ DTree − and w u | v, k from [0.0,1.0] for Σ DTree +.
