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> seems to me that the the act of compiling knowledge into probabilistic models is still more art than science

Because there’s no modularity: writing probability models is still like using unstructured assembly.

Having written a handful of probabilistic programs, I don’t think there’s no modularity at all. You can still abstract a complex stochastic process behind a function. I think it’s more that certain tasks are necessarily antimodular, eg doing conditional inference can introduce unexpected dependencies between modules, see d-separation [0].

[0] https://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html

Well, as part of the probabilistic programming community, I can at least report that inference programming and model synthesis are problems we're actively working on!

Ok but we agree. There’s a surface modularity in terms of syntax but anything introducing nonlinear covariance among variables is a death sentence absent reparametrization. The latter strategy is often formulaic and could be automated but the antimodularity remains

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