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avibryant's profile
Avi Bryant
Avi Bryant
Avi Bryant
@avibryant

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Avi Bryant

@avibryant

Happiest when working on Random Forests from random beaches.

Galiano Island
avibryant.com
Joined November 2006

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    1. Avi Bryant‏ @avibryant Jun 1

      Avi Bryant Retweeted Darren Wilkinson

      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 …

      Avi Bryant added,

      Darren Wilkinson @darrenjw
      Monadic probabilistic programming in #Scala with Rainier https://darrenjw.wordpress.com/2018/06/01/monadic-probabilistic-programming-in-scala-with-rainier/ …
      2 replies 4 retweets 30 likes
      Show this thread
    2. Avi Bryant‏ @avibryant Jun 1

      Also, I have to ego-quote this bit, because that style of gap-bridging is exactly what I have spent a career striving to do from various angles and it’s neat to have it recognized.pic.twitter.com/eESJam9Nj6

      1 reply 1 retweet 17 likes
      Show this thread
    3. Sean Talts‏ @seantalts Jun 4
      Replying to @avibryant

      That's pretty harsh on Stan, though mostly on syntax? We're working on a proposal for Stan 3 which seeks primarily to address the code modularity issues (and will add some syntactic niceties like type and block inference); curious if that scratches the itch, or what's missing...

      1 reply 0 retweets 0 likes
    4. Avi Bryant‏ @avibryant Jun 5
      Replying to @seantalts

      I don't know precisely what @darrenjw had in mind. I would agree that Stan doesn't feel like modern, full-fledged PL (compared to say, Elm, which is also tiny and special purpose but I would be happy to call "elegant"). But I wouldn't have assumed that was even a goal?

      1 reply 0 retweets 0 likes
    5. Avi Bryant‏ @avibryant Jun 5
      Replying to @avibryant @seantalts @darrenjw

      2/3 Stan is obviously extremely useful and successful in its niche, and I think the focus and investment there has been appropriate. The itch I was trying to scratch has to do with lowering the bar for production engineers to work with bayesian models,

      1 reply 0 retweets 0 likes
    6. Avi Bryant‏ @avibryant Jun 5
      Replying to @avibryant @seantalts @darrenjw

      and in that context, *any* non-standard language is huge roadbump. The dependency on the gcc toolchain is also a (not insurmountable, but real) barrier to deployment.

      1 reply 0 retweets 0 likes
    7. Sean Talts‏ @seantalts Jun 6
      Replying to @avibryant @darrenjw

      I see. So I guess to summarize the Stan perspective (as best I can): functional is not strictly more elegant than imperative, learning a C derivative is the least of a budding statistician's problems, and inference algorithms are probably not meant to be deployed (insights are).

      1 reply 0 retweets 0 likes
    8. Sean Talts‏ @seantalts Jun 6
      Replying to @seantalts @avibryant @darrenjw

      My personal views on those issues have definitely shifted over time (not sure where I'm at right now). Deployment is perhaps most arguable - their view stems from a fairly sturdy belief that inference algorithms aren't perfect and diagnostics should be inspected with ~every fit.

      2 replies 0 retweets 0 likes
    9. Avi Bryant‏ @avibryant Jun 6
      Replying to @seantalts @darrenjw

      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.

      1 reply 0 retweets 0 likes
      Avi Bryant‏ @avibryant Jun 6
      Replying to @avibryant @seantalts @darrenjw

      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.

      10:59 AM - 6 Jun 2018
      • 1 Like
      • Sean Talts
      1 reply 0 retweets 1 like
        1. New conversation
        2. Avi Bryant‏ @avibryant Jun 6
          Replying to @avibryant @seantalts @darrenjw

          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.

          1 reply 0 retweets 0 likes
        3. Sean Talts‏ @seantalts Jun 6
          Replying to @avibryant @darrenjw

          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

          1 reply 0 retweets 1 like
        4. Sean Talts‏ @seantalts Jun 6
          Replying to @seantalts @avibryant @darrenjw

          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!

          1 reply 0 retweets 1 like
        5. Sean Talts‏ @seantalts Jun 6
          Replying to @seantalts @avibryant @darrenjw

          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 @darrenjw's comments.

          1 reply 0 retweets 0 likes
        6. Avi Bryant‏ @avibryant Jun 6
          Replying to @seantalts @darrenjw

          Awesome! Look very forward to it.

          0 replies 0 retweets 0 likes
        7. End of conversation

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