Hi,

I am looking for a simple way to generate a random number according to a given distribution.

The function is not easy to invert and I do not want to do it. I will be happy to find a numerical method.

It could digitize the function at some discrete points and make the inversion numerically, as it is done in

I am working in the hadron spectroscopy, that is sometimes about large datasets and a lot of MonteCarlo.

The common tool is ROOT with C++ https://root.cern.ch/, but it is C++.

I also use Mathematica, but it is slow and commercial.

Julia might be a good option in between.

As an example, I want:

3 variables (will be colums, say A B C), 10e7 rows for random values distributed according to some known functions.

A ~ sqrt(1-1/a), in the interval 1:5,

B and C are correlated: B x C ~ sqrt(b)*(b+c)^2, b in 2:6, c in 0:10

Btw, would you suggest to use DataFrames of work with a nested array?

Is there a way to apply function on columns over rows of the matrix? Something like

```
map(x->x[1]+x[2], myMatrix)
```

Thanks