Hello,

I’m currently working on developing a Bayesian model to estimate certain linear regression parameters, and I’ve encountered a challenge regarding how to model the variance of the dependent variable (y) as it changes with the independent variable (x). I understand that it’s common practice to use relative residuals by normalizing the observed data within the likelihood function. However, I’m facing a difficulty in PyMC where I can’t directly normalize the observed data and then return it to its original form afterward, right?

Do you have any suggestions on how I could address the heteroscedasticity using PyMC?

These are my previous questions, provided for context:

Question 1

Question 2

I still working in the same model (I know, long time working in the samething…)

Thank you!