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".
-
Show this thread
-
If anyone's interested in getting early access to the repo and willing to give us some feedback in advance of the public release, please get in touch (here, or avi@stripe.com).
8 replies 1 retweet 11 likesShow this thread -
Replying to @avibryant
How easy is it to add new inference algorithms? How easy is it to compose existing inference algorithms?
1 reply 0 retweets 0 likes -
Replying to @zaxtax
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.
1 reply 0 retweets 0 likes
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.
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.