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Theoretical probability density function models are fitted against numerically simulated distributions within a maximum likelihood context.
The model is constructed within the maximum likelihood context and implemented with the EM algorithm.
The model and procedure are derived within the maximum likelihood context and implemented with the EM algorithm.
Our model is founded on population genetic properties of genes that are segregating in a pedigree, constructed with the mixture-based maximum likelihood context and implemented with the EM algorithm.
For secondary structure informed analyses, we tested 6- and 7-state rDNA-specific models [ 109] in a Maximum Likelihood context along with the traditional nucleotide (4x4) model in RAxML 8.1 [ 110] run on the CIPRES Portal [ 111].
This model is developed within the maximum likelihood context, implemented with the EM algorithm for estimating the population genetic parameters of QTL and the simplex algorithm for estimating the QTL genotype-specific parameters of power curves.
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The model and the data have been previously used in a maximum likelihood estimation context in [ 17].
Then substitution histories were computed using maximum likelihood and a context-dependent evolutionary model.
To enable these genome-wide analyses, we inferred substitution histories using maximum likelihood and a context-dependent model of evolution with the PHAST package (Hubisz et al. 2011).
The minimax designs and unbiased designs are found for maximum likelihood estimation in the context of both prediction and extrapolation problems.
To quantify evidence for BDS, we inferred substitution histories using an efficient maximum likelihood approach with a context-dependent evolutionary model.
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