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Systems involving direct and indirect associations between variables can be described using path models.

The advantage of the Bayesian Networks approach is that the models are easy to interpret and the uncertainty inherent in the relationships between variables can be expressed in terms of probability.

Since Latin hypercube sampling is more efficient and robust than simple random sampling [29, 30], and correlations between variables can be considered by using the correlation matrix during the pairing process [31], Latin hypercube sampling with the Cholesky decomposition method is used to generate samples according to their correlation.

Thus, direct and indirect relationships between variables can be assessed.

As each genotypic variable is tested individually, no combinatorial dependencies between variables can be detected.

Also, interactions between variables can be identified and quantified by this technique [ 29].

Thus, the nature of the correlation between two variables can be visualised through the angles between two vectors): if the angle is sharp, the correlation is positive, if the angle is obtuse the correlation is negative and if the angle is right the correlation is null.

Given a Pearson correlation matrix P between a set of variables, the matrix of β-weights between all variables can be computed from R = P - 1 by β ij = -R ij /R ii.

SEM can be used to test the theoretical assumptions between observed variables and latent variables and to verify whether hypothetical relationships between observed variables can be supported by empirical data.

By Copulas, the dependences, or correlations between random variables can be set up.

The correlation between both variables can be characterized by the correlation coefficient defined as [35] (33).