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The computational cost and the model size are also examined.

However, in genomic-enabled prediction (when p >> n), only the Bayesian probit model is frequently implemented, given that Bayesian methods that introduce sparseness through additional priors on the model size are very well-suited to this problem.

BMA posterior inclusion probabilities for model variables, along with regression coefficients and 95%% credible intervals, as well as graphs presenting information regarding the sampling process and posterior distribution of model size are presented in the Additional file 7. Figure  4 presents the summary and comparison of the variable selection methods used in this analysis.

In what some would describe as the heyday of modelling – when supermodels such as Linda Evangelista, Cindy Crawford and Claudia Schiffer reigned supreme – the normal model size was more like an eight or a 10.

Therefore, the model size is reduced before the optimization takes place.

We observe that if the model size is large enough for the classical moduli to be size effect free.

Since the number of components dominates the size of the traditional model equations, a significant reduction of the model size is obtained through this new technique.

The dependence of model uncertainty on the model size is also investigated by exploring skeletal models containing different number of species.

This situation is more likely to occur when the reduction in the model size is large.

The joint model size is 50 mm in diameter and 144 mm in length.

The restriction in model size was applied in order to decrease the computational cost.