Sampling error when using complex tensor function as distribution parameter

I have hit a wall trying to construct a particular model, and I wanted to ask for some help troubleshooting this. I am making a model which calculates probabilities from a normal CDF with mean/sd that are PyMC3 distributions. I transform those probabilities and try to use those as the parameters for an output distribution. Whenever I try to sample from that distribution I quickly receive the “ValueError: Mass matrix contains zeros on the diagonal.” error.

I simplified the model down as far as I could while still getting this error. I can sample through my function and generate what looks to be perfectly fine samples of the u/“spprop2” variable, but as soon as I try to use that as a parameter for a subsequent distribution this error occurs. Most of the hits for this error seem to be about overflows resulting from extreme priors. I don’t have any observedRVs in my model at this point, and everything up to the K distribution behaves like I would expect and the samples of u look perfectly fine and don’t seem like they would cause any overflows.

I would appreciate any feedback on this and ideas about how to resolve this error.

gates = np.log10(np.array([0, 500, 3000, 7000, np.inf])) contains -inf and inf, would that be the problem?

Removing those does resolve this issue, although I’m not sure why. The function f() does what I was hoping it would with infinite input there. The inf values in gates should disappear outside of the f() function, as they act to allow calculating a probability for all four splits of the input normal distributions across the entire real number range (the erf() handles them appropriately). If I make the values into very small and large numbers (even finfo(float64).eps and finfo(float64).max) it fixes this issue, so that is good.

Ultimately, I am not sure why this fixed the problem because the f() function is returning identical values, I’ll chalk it up to some Theano thing. Thank you for the pointer.

Altought the function is returning same value, it can make a difference as it’s trying to take gradient of the function also. If some intermediate steps have inf I imagine it would give an error for NUTS (no error for theano necessary).