

MSR's Probabilistic Programming Language R2 Released - cf
http://research.microsoft.com/en-us/projects/r2/

======
pathikrit
I found this paper really illuminating:
[http://research.microsoft.com/pubs/208236/asplos077-bornholt...](http://research.microsoft.com/pubs/208236/asplos077-bornholtA.pdf)

~~~
sytelus
Our code often involves quantities which have intrinsic errors (like readings
from accelerometers or photo-sensors or even just plain data like averages).
The probabilistic framework enables us to computer with these kind of
quantities that have uncertainties embedded in to them.

Every programmer should read this paper. It's one of the nicely written paper
than pretty much anyone can understand.

~~~
pathikrit
Are you aware of any open source implementation of it? I am working on a Scala
`Uncertain[T]` library...

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superfx
Main paper here:

[http://research.microsoft.com/apps/pubs/?id=211941](http://research.microsoft.com/apps/pubs/?id=211941)

------
paperwork
I don't see any reference to infer.net [1]. Aren't both of them related?

[1] [http://research.microsoft.com/en-
us/um/cambridge/projects/in...](http://research.microsoft.com/en-
us/um/cambridge/projects/infernet/)

edit: typo

~~~
ot
They are related in that they are both probabilistic programming languages,
but the similarities stop there.

First, infer.net is a DSEL of C# implemented as a library, while this is a new
language. More substantially, infer.net is designed to use variational
inference, which limits the class of graphical models that can be supported,
while R2 seems to be based on sampling hence has no limits on the models it
can represent. A more similar approach would be Church
([http://projects.csail.mit.edu/church/wiki/Church](http://projects.csail.mit.edu/church/wiki/Church)),
a probabilistic language based on Scheme.

Of course, the downside of sampling is that inference is orders of magnitude
slower, and there's no principled way to know when it converges, that's why
many people prefer variational inference.

~~~
eli_gottlieb
Of course, both variational inference and sampling-based inference are
exponentially faster than actual exact probability calculations.

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bayesianhorse
Hm, the obvious criticism is that it's not open source, unlike STAN and PyMC,
and "only" has the advantage of being about twice as fast.

I haven't read the article thoroughly enough to know if the samples are of the
same quality.

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jdeisenberg
The name seems to have been ideally chosen to cause confusion between it and
the existing R language.

~~~
gwern
It wouldn't surprise me if they had intended it as a homage/boast - both are
statistics-related, after all.

