Have there been any attempts to bring symbolic regression techniques to PyMC? I was looking into Bayesian symbolic regression and I was only able to find this one paper and, if I understand correctly, they had to use an unusual type of MCMC sampler. I was looking for implementations in any of the big PPL’s, but wasn’t able to find anything. If they exist, they are not publicized.
I strongly suspect that my best option is to use a non-parametric regression technique in a model during inference and, outside the model, use a symbolic regression toolkit like PySR on the conditioned non-parametric regressor. Has anyone attempted something like this before?
Thank you all for your time
You should check out the work of MilesCranmer He has a package for symbolic regression in Julia - SymbolicRegression.jl. He’s also got a package called PySR which seems a first glance to be implemented in Python.
Thank you for the reply! I am familiar with PySR and I agree that it could be useful to me. The reason for my question is that PySR only works when you have clearly defined input and output data. However, I am more interested in doing symbolic regression with latent variables as a function of input data in a PyMC model and I’m not sure what the best way to accomplish that is. It seems like there’s no good way to include a symbolic regressor in a PyMC model. Therefore, my first thought is to use a nonparametric regressor like a Gaussian process and, after sampling, use PySR to get an approximate functional form of the GP outside of the PyMC model. I was wondering if anyone had attempted something like this and if they had any experiences they wanted to share.