Basically the question is: how do I use a function which does not act on pytensors, but instead on tensorflow tensors?
I am using a gaussian process to fit a function with GPFlow which gives me attenuation as a function of absorption. Absorption is due to a few things which make up the material, so I place priors on the constituents and do a linear combination taking into account the varying absorption at different wavelengths.
# some numpy array with the same shape as data wavelength_dependent_absorption_coefficient = [...] with pm.Model() as model: constituent = pm.Normal('constituent', 1, 0.1) absorption = wavelength_dependent_absorption_coefficient * constituent y = pm.Normal('y', mu=gaussian_process(absorption), sigma=0.1, observed=data)
Unfortunately this doesn’t work, giving an error like:
ValueError: setting an array element with a sequence. This issue is discussed inside the ‘using a “black box” likelihood’ example. However, ideally, I’d like to find a simpler solution. I’m very willing to implement the gaussian process in pymc but at the moment would have the same problem with calling an external function.