I'm super excited by how close we're getting to releasing some work I've been doing at Stripe on a static compute graph + bayesian inference library for Scala. Depending on your background, you might think of it as aspiring to be "TensorFlow for small data" or "Stan on the JVM".
We've really given no thought to composing inference algorithms so I wouldn't expect that to be easy. In terms of adding new inference algorithms, I guess it would depend a lot; eg particle filtering, I think would be pretty hard to add; more variants of MCMC would be easy.
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Ironically the starting point was a Scala implementation of https://dl.acm.org/citation.cfm?id=2804317 … which has a focus on composition and also particle filtering, but we've diverged greatly from that now.
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