The “correct way” to handle discretized data from a continuous process is to integrate out the density between bin edges.
PyMC can give you the probability of “rounded”, “ceiling”, “floored” data, but not arbitrary bins yet.
An example is given in this thread:
For a manual implementation of arbitrary bins you may check this notebook: Estimating parameters of a distribution from awkwardly binned data — PyMC example gallery
However I am not sure you are writing the correct PyMC model to begin with. I suggest you do some parameter recovery study with simulated data before discretization to see if your model works correctly in that case. Then you can add discretization and repeat the experiments with the same continuous likelihood and/or the right likelihood for the binned data.
Sometimes the distortion caused by binning can be ignored altogether if you have a lot of data / a well identified model.