Sentence examples for model comparison algorithms from inspiring English sources
For more complex models, particularly with many modes, more complex model comparison algorithms might be required (see, for example, [ 14]).
A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms.
The retained samples were used as basis for recursive causal structure search via IC algorithm, model comparison, and inferences about the parameters of the model fitted conditionally on the selected causal structure.
If structural models are available, then structure comparison algorithms may provide much higher confidence in assigning residue-residue correspondences than sequence-based algorithms alone.
Uniquely, Dizzy-Beats provides model comparison and parameter inference functions through the nested sampling algorithm in a graphical application.
Abstract: We consider model comparison when test statistics are derived from the output of a Markov chain Monte Carlo algorithm.
The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model.
The COBRA Toolbox 2.0 implementation of the SMILEY algorithm (growthExpMatch) was used to predict sets of gap-filling reactions for each false negative model comparison.
Despite the growing number of machine-learning algorithms that have been developed, relatively few studies have provided a comparison of an array of different learners — typically, model comparison studies have been restricted to a comparison of only a few models.
