# Modelling Correlation in Random Effects¶

As shown in Inter-Subject Variation (ISV), we can use random effects to account for variability in model parameters between individuals in a population. In that example, we demonstrated the principle for a single parameter whereas we now consider the case where two or more parameters vary between individuals. In particular, we are interested in what happens when pairs of parameters vary in similar ways, for example when they have a common underlying cause.

In the following examples we model inter-subject variability in both `m[CL]`

and `m[V]`

using a combined proportional and additive noise model to generate observations for 200 individuals, and fit various models to the synthesized data.