Hi,
Is there any way we can calculate the cumulative distribution function (CDF) of the data using PyMC distributions? I need the CDF values for the likelihood functions of the archmedian copula. I attempted to use TensorFlow distributions with the TensorFlowOp; however, I encountered numerous errors.I checked some links (python - How to get the Cumulative Distribution Function with PyMC3? - Stack Overflow) but couldn’t fully grasp the concept.

If you have any insights or workarounds for efficiently computing CDF values using all the PyMC distributions, I would greatly appreciate your expertise on this matter.

Hi,
Thanks for your reply. This is the function I was looking for. I am trying to implement it for copula parameter estimation. I understand it performs element-wise operations. I am struggling a bit to implement the CDF function in the code. The code is given below. Please advice how to make this work.

##Data
import random
u =
v =
for i in range(0, 100):
u.append(random.randint(0, 100))
v.append(random.randint(0, 100))