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Stan: A (Bayesian) Directed Graphical Model Compiler

Here’s Bob’s talk from the NYC machine learning meetup. And here’s Stan himself:

5 Comments

  1. Mark J says:

    Four pages dense with problems that had to be solved in order to get this to work — wow, I hope you guys are being paid by the hour! (smile) Seriously, it looks great; how long until it will be usable by us mere mortals? Say g’day to Bob from me.

    Typo bottom of page 19, I think.

  2. I need this Software because I am a Professor of Animal Science in Camagüey University, in Animal
    Genetics.

  3. [...] Stan: A (Bayesian) Directed Graphical Model Compiler [...]

  4. cjs says:

    Your source code is currently on code.google. After you release it, will it be moved to github/bitbucket? In case others want to submit pull requests to add to it, or fork it for their own use?

  5. Richard D. Morey says:

    In my experience, when you transform parameters, like variance parameters, by taking the log, that gives you some very steep gradient functions in some parts of the parameter space. Because HMC is a discrete approximation, this can lead to nasty behavior of the sampler. Does Stan/NUTS handle that well? I typically just integrate the constrained parameters out analytically, which solves the problem – but with a general-purpose solution like Stan, that’s not really an option.

    (By the way, I can’t wait to try this software.)