Sentence examples for model comparison based from inspiring English sources

DVR 1 from the 2TCM were considered as reference values for model comparison based on model preference results across brain regions.

A Bayesian approach to model comparison based on the integrated or marginal likelihood is considered, and applications to linear regression models and nonlinear ordinary differential equation (ODE) models are used as the setting in which to elucidate and further develop existing statistical methodology.

To improve convergence, the between-study variance could be determined by non-iterative methods [24], but these approaches are difficult to combine with the maximum likelihood approach for estimating the weight function, and with model comparison based on the Akaike criterion.

Bayesian model comparison, based on the increase in log-evidence across the six models, is shown in Fig.  4B.

A model comparison based on AICc, described above, determined which GWL parameters (w, c, both, or none) were required to vary across strains in order to describe changes in σ as a function of μ.

A zero-inflation parameter was included when indicated by model comparisons based on AIC.

However, in this case, model comparisons based on Bayes factors indicated superiority of simultaneous models over recursive parameterizations.

However, there are very few reports on model comparisons based on real data from livestock [ 27, 28].

In general, phylogenetic model comparisons based on simulations (e.g., [ 33]) tend to be more sensitive than comparisons based on model fit (e.g., [ 34]).

Note that model comparisons based on likelihood ratio tests are not directly applicable here, as the set of models of interest do not all form a nested hierarchy.

Each sub-sampling was run three times for each model allowing 27 pairs of model comparisons based on the thermodynamic integration value.