Jonathan Feinberg

Your are not missing anything. Quadrature rules does not garanty that 0 should be included. For distributions symmetric around origo (like yours), 0 is typically included at even order. Your...

From the paper they say that they just add 0 for every odd terms. For your one-dimensional case, I don't see why you can not do this manually after quadrature...

Okay. Take a look at `help(chaospy.quadrature.hypercube.hypercube_quadrature)` in a Python REPL. It is a helper function that transforms 1-D quadrature fuctions into N-D. So if you wrap your quadrature rule with...

From a practical point of view, you need to include the dependencies into a joint distribution to make it all work: distribution = cp.J(b1, b2) This joint distribution can be...

Okay, working through it, it is becoming clear to me that the approximation methods used to get raw statistical moments does not really work for discrete distributions. Long term, this...

Okay, I got through the math, and it got a bit more messy then I anticipated. I think the solution works as intended (though you might want to double check)....

Yes, that is what I get as well. As I noted, Bernoulli polynomials can theoretically only have 2 mutually orthogonal polynomials associated with it. Beyond that, it brakes down. It...

You definetly can model conditional distributions in chaospy, but it is a big topic that deserves a lot more attention in the documentation than what is actually there. I really...

In chaospy dependencies are declared through parameters. So when you say "conditional lognormal" that can either be declared as: ```python chaospy.LogNormal(mu=chaospy.Weibull()) ``` or ```python chaospy.LogNormal(sigma=chaospy.Weibull()) ``` The latter is illigal...

> I am doing a problem of UQ, using the tensor product expansion method in chaospy, but the function orth_ttr does not seem to have it, and how to substitute...