I’m currently working on a hierarchical implementation of a linear regression model to predict electricity consumption values for buildings.
As of now, I only tried implementing Gaussian priors, but I would be interested in testing how L1 regularisation performs. I read in different places that it’s possible to implement L1 regularization in pymc3 by using Laplace priors in place of the Gaussian.
From a building physics point of view, it would make sense that coefficients for some of the variables (e.g. outdoor temperature) would be constrained to be non-negative, so the question is: is there is a prior that can allow me to implement L1 regularization, and at the same time forcing the coefficients to be non-negative? Would it be some kind of special case of the Laplace prior?
I didn’t include a code snippet of my model here since I think it would not add much to the question, but if needed I can add it in a following edit.
Thanks in advance for your help!