I have just joined a new group with a new employer, and the manager of this group is keen to have everything done in Python. I don’t find this ideal, but I understand his motivation, and it’s not worth pushing back on; i.e. I can live with it. However, I expect a lot of the work to involve probabilistic programming. I have explained that while this, yes, would likely be done in Stan, we can use Stan through PyStan, which keeps at lest within the letter of the guidelines (I have no experience with PyStan, but I am sure it is OK).
However, he immediately mentioned TensorFlow Probability. I think he is under the impression that this is a more authentically Python native package. I’m not interested in pushing back on this idea, however I’m interested to know how it compares to Stan as a general tool for building generative bayesian modelling. I don’t want to be stuck with bad tools, obviously.
I have no opinion about this. I don’t know it (it has autodiff, which is good, allows, HMC, etc. but it does seem to be focussed on probabilistic deep learning, an area about which i know very little). I have just looked briefly at it, and all I see is the standard mess of python library calls, which are not to my taste, but if this is only a superficial complaint about syntax, I can live with it.
Thus, anybody have, or know where I can find, informed technical comparison. Or alternatively, does anyone have a one-line dismissal, or endorsement, etc…
All comments welcome.