I came across a paper describing “Bean Machine” a new thing from FB built on PyTorch for declarative probabilistic programming,
It seems a major innovation is explicit use of model dependencies to do various things that aren’t otherwise possible e.g. block updates which are better for correlated variables (?). What caught my eye was the custom proposers which they suggest are beneficial for difficult multimodal problems.
Oddly enough they mention using PyTorch as beneficial compared to Stan, but their benchmarks show Stan has higher neff/s for the tested models.