Darren’s blog was very helpful as I got started learning about bayesian inference, so it’s very neat to see Rainier appear there. (Also, he’s been a wonderful beta tester and now contributor. Thanks!)https://twitter.com/darrenjw/status/1002685644777377799 …
Again, I think Stan made the right choices for its audience. I agree that it comes down to deployment. My question is, *if* you decided to deploy inference at scale, *then* what would you want? I do think a familiar production environment is part of that.
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Functional vs imperative isn’t that interesting a question to me here aside from the case where you already have engineers who are used to working in a particular environment and paradigm - at which point being as native to that env as possible is a big win.
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Like, we could geek out about ways Stan could be a better PL, as a PL, but I think we agree that “being interesting to PL geeks” shouldn’t be one of Stan’s goals. “Being interesting to Scala geeks” *is* one of Rainier’s goals.
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We have a debate pretty often regarding whether you can entice engineers into learning how to do good statistics by making it closer to something they know, much like the way you're trying to do with Rainier. I think almost everyone disagrees with you and me on this issue :P
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I have my beliefs here but tend to go with theirs since they've been in that intersection a lot longer than I have. This should be a good experiment!
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Also it's pretty relevant to my current project to geek out about ways it could be a better PL. I will post a link to a Stan 3 proposal once I get it out! Would love to have your and
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Awesome! Look very forward to it.
End of conversation
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