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This alignment, together with the corresponding subset of the tree and a codon substitution model, was used to create a likelihood function (LF) which models selection using the PyCogent framework [ 73].
Briefly, these stages consist of (1) segmentation of soft tissue, (2) registration of the left images to the right images, (3) bilateral subtraction of the coregistered images, (4) filtering of the images to enhance the signal and texture, (5) feature extraction, (6) multivariate models selection using a train set, and (7) model evaluation on a test set.
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In theory, at infinite sample size, model selection using BIC selects the true model if it is included in the search space [56].
A stepwise model selection analysis was performed, yielding the same markers that were selected by stepwise model selection using an ordinary Cox model (data not shown).
Performing model selection using Bayes factors is particularly useful when dealing with a few models only.
We therefore chose to perform model selection using the AIC.
STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm.
Model selection using the Poisson regression approach was discontinued at this stage because more complex models no longer converged.
This experiment was repeated 1000 times and the percentage of optimal model selection using these three criteria was obtained.
While model selection using compositional modeling has been shown to be intractable, it is tractable when all model fragment approximations are causal approximations.
Model selection using simultaneous autoregressive models was used to generate models describing the microhabitat use of each of three salamander age classes (adults, juveniles, and metamorphs).
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