Jonathan Feinberg

Sorry the late reply. My kid's kindergarten has been hit by the omicron variant and we're stuck in quarentine. Rosenblatt-transformation is in its essence a variable switch. Instead of looking...

Hi! Sorry for the delayed response. In chaospy handling of dependencies relies on the ability to create Rosenblatt transformation. In essense this means the dependencies has to be decomposable into...

You might want to check out [Isserlis' theorem](https://en.wikipedia.org/wiki/Isserlis%27_theorem) on how to calculate the moments you need.

Good for you. :) I think getting to specific with the dependent distribution doesn't make too much sense to merge. Fork sounds like a good way to go. I'll likely...

1) chaospy considers dependency that can be formulated into a conditional decomposition: `p(X)*p(Y|X)*p(Z|X,Y)*...`. So in theory everything. In practice, some messy maths might be involved (which is why I am...

In general terms being able to to do conditional expectation on a stochastic dependent variables is hard. There are lots of example where it is fine, but there are plenty...

Here is a working example: ```python import numpy as np import chaospy as cp # correlated variable: cov = [[1, 0.5, 0.25], [0.5, 1, 0.5], [0.25, 0.5, 1]] mean =...

Ah, good point. I didn't think the example through. So the `qq` variable contains a decomposition on the form `[f0(q0), f1(q0, q1), f2(q0, q1, q2)]`. That means the last entry...

Yeah, you got it. `[1, 1, 0]` should correspond to `q0, q1`. But yeah, I might have mixed that up above. My time is a bit limited, and I am...

I haven't tested you code, but yeah, that looks about right. Good going.