Sentence examples for maximum likelihood convergence from inspiring English sources

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SAS Version 9.1 was used for Box-Cox transformations, Newton-Raphson maximum likelihood convergence, pre-diabetic phenotype associations, Fisher's exact test, as well as for all SNP association analyses.

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Several (mathematical) details of the procedure, such as approximate equivalence to maximum marginal likelihood, convergence criteria and various types of parametric priors are further discussed in [ 3].

We estimated risk ratios using binomial regression with a log link; using either maximum qualified likelihood (Fisher scoring) or maximum likelihood, depending on convergence.

However, the computational approach used in Latent GOLD is very stable and offers good performance in terms finding the maximum likelihood solution and convergence.

The algorithm then iterates the steps of (i) aligning the graphs using the estimated parameters and (ii) estimating the maximum likelihood parameters until convergence.

Variance components and were estimated by restricted maximum likelihood and at convergence of the variance component estimates, SNP effects were estimated by GBLUP as the regression of SNP effects on additive genetic merits as described in Taylor [ 69].

When the log-binomial model fails to provide the maximum likelihood estimates because of convergence or other numerical problems, one may obtain a generalized estimating equation (GEE) estimator by fitting a robust log-Poisson model (Zou 2004).

In all cases multiple independent runs of the CodeML program were conducted (minimum triplicate) to ensure convergence of maximum likelihood estimates of parameters.

The model parameters are learned using an Expectation Maximization (EM) algorithm, which on convergence provides maximum likelihood estimates of the transition matrices, module mean and variance and module assignments in both extant and ancestral species.

This is analogous to convergence to the maximum likelihood using more traditional estimation algorithms, with the exception that there is not convergence to a single maximum likelihood, but rather a probability distribution from which various useful statistics can be derived (e.g., mean, median, and SD).

Convergence rates of maximum likelihood estimates (MLEs) differ from parameter to parameter, which is not well explained in the literature.

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