Hello, I am setting up an inference problem where I have about a dozen (lets say N) variables and each variable has 3 properties. Each property has a uniform prior with different bounds.

Until now I have been setting the problem up by defining Nx3 independent variables with uniform priors. But actually for each variable the three properties are highly correlated.

I came across several examples that deal with correlated variables (1, 2, 3, 4) but none actually addresses uniform priors. It seems like the `pm.MvNormal`

distribution with an appropriate correlation matrix would do the job, but in my case the variables are not normally distributed.

Is there a simpler way to accomplish this, for example to assign correlation between the dimensions of a variable? From what I got, when assigning dimensions, these are still independent.

Thank you for the help,

Alexis