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These confidence intervals could not be computed for the MLE because the convergence problems experienced by the maximization algorithm made the computation of q infeasible.
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This may be controlled for by bounding the slope parameter in the maximization algorithm.
Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expectation Maximization algorithm (EM) which maximizes the log-likelihood criterion.
The parameterization of this parser is optimized automatically by assigning latent variables to each non-terminal node and estimating the parameters of the latent variables by the expectation maximization algorithm.
The reference point set is represented as gaussian mixture model centroid and fitted to the target point set by the expectation maximization algorithm.
The model parameters are trained by the expectation maximization algorithm.
Next, maximum likelihood estimation was used to impute the missing values, which was implemented by the expectation maximization algorithm.
This criterion is a generalization of the function maximized by the Expectation-Maximization algorithm [De .pdftertextitet al., 1977].
Estimated haplotypes were constructed for each sample by using the expectation maximization algorithm implemented in the haplo.stats package within the R statistical program.
The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results.
Haplotype frequencies were estimated by the expectation-maximization algorithm using the tagSNP software.
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