
Edward is officially moving into TensorFlow - pepopep
https://discourse.edwardlib.org/t/edward-is-officially-moving-into-tensorflow/387
======
modeless
"Edward is a Python library for probabilistic modeling, inference, and
criticism. It is a testbed for fast experimentation and research with
probabilistic models, ranging from classical hierarchical models on small data
sets to complex deep probabilistic models on large data sets. Edward fuses
three fields: Bayesian statistics and machine learning, deep learning, and
probabilistic programming."

[http://edwardlib.org/](http://edwardlib.org/)

~~~
michaelmior
It's unfortunate that people often forget to include context like this in
announcements. Thanks for sharing!

~~~
eatbitseveryday
The announcement is on edwardlib.org (the URL shared on HN). Wouldn't that be
an obvious connection? A project making an announcement about itself on its
own website, seems natural to me.

~~~
foepys
The announcement should at least include a link to an explanation on the main
project page. Due to only being posted on discourse, there is no direct link
to go to the main project page, only by manually editing the URL or googling
for Edward. Even the logo in the top-left corner only leads to the discourse
overview page.

Edit: Just editing the URL leads to an HTTPS error because the certificate is
only valid for *.github.io; you need to use HTTP.

~~~
eatbitseveryday
I agree the connection could be smoother to get there... _shrugs_ not really
something I'm spending cycles being concerned about.

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cshenton
I've had the pleasure of working with Edward for the last couple of months,
and it's been the first PPL I've felt comfortable building a stable production
codebase on top of.

It strikes a great balance between feeling like you're programming with
probability distributions, and providing ways of diving under the hood to
improve performance when you need to (like tweaking the underlying tf
optimizer, or being able to implement your own distribution to use like a
native one).

If such a library didn't exist, I would have needed to build my own. Congrats
on the move to tf.contrib.

~~~
nextos
What kind of production stuff are you developing?

I'm a probabilistic programming enthusiast, and I had the impression it's
still an open research field.

~~~
cshenton
Time Series forecasting as a service. It's an area where careful treatment of
error distributions matters a lot. There's a link in my HN profile, though the
page won't be up for another week.

~~~
nextos
Absolutely! Great stuff.

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catpower
Super interesting given that the main Edward competitor, PyMC3, was built on
Theano which is discontinuing as of a few days ago.

Hopefully not a trend of everything being subsumed by Tensorflow.

~~~
plexicle
"Hopefully not a trend of everything being subsumed by Tensorflow."

Why?

~~~
hulistinist
I don't have anything against Tensorflow, but I've learned over the years that
it's good to have competition in numerical libraries and software so there's
replication of results. Try your analysis using one library, and corroborate
it using another one. When a field is dependent on one major piece of
software, it's more susceptible to bugs--a programming bug becomes a misguided
line of research for a whole field.

Edward has been a promising addition to the PPL landscape. I actually
preferred using it with Theano when I used it but that was a year ago, and it
seems to have been developing rapidly. I have mixed feelings about this
announcement, although to be honest I don't totally even really understand all
the implications of it. In some ways I'm not sure how much Edward
incrementally adds above and beyond TF; it has occupied a niche between
something like TF and Stan or PyMC which is fine enough but I've sometimes
wondered if it was sustainable in the long run. I have appreciated it being
around, though, and have hoped it would continue to develop.

~~~
wodenokoto
Who's the numpy competitor? Are we suffuring due to lack of one?

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dasmoth
_Who 's the numpy competitor?_

R? Eigen? Neanderthal? HMatrix?

(Yes, none of these are exactly 1:1 equivalent with numpy, but there
absolutely are options. And from my point of view, having some options which
aren’t tied to Python is healthy).

~~~
dragandj
Thanks for mentioning Neanderthal!
[http://neanderthal.uncomplicate.org](http://neanderthal.uncomplicate.org)
[http://github.com/uncomplicate/neanderthal](http://github.com/uncomplicate/neanderthal)

It aims to have more features, and more speed than numpy, with Clojure, on the
JVM + Nvidia + AMD + Intel.

Also relevant here is Bayadera, Clojure/GPU Bayesian modeling lib for the JVM:
[http://github.com/uncomplicate/bayadera](http://github.com/uncomplicate/bayadera)

~~~
nextos
Bayadera looks very cool. Do you have any examples involving complex
hierarchical models, and performance vs Stan or PyMC?

~~~
dragandj
Yes. See
[https://github.com/uncomplicate/bayadera/tree/master/test/cl...](https://github.com/uncomplicate/bayadera/tree/master/test/clojure/uncomplicate/bayadera/examples/dbda)

and

[https://www.youtube.com/watch?v=bEOOYbscyTs](https://www.youtube.com/watch?v=bEOOYbscyTs)

[https://www.youtube.com/watch?v=TGxYfi3Vi3s](https://www.youtube.com/watch?v=TGxYfi3Vi3s)

~~~
nextos
Thanks!

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vslira
Does anyone recommend a source akin to “statistical rethinking”, but in
Python/pymc/edward?

That book’s balance between theory and practice is remarkable, but im no fan
of R :/

~~~
colcarroll
Osvaldo Martin has ported (most of?) the book to PyMC3:
[https://github.com/aloctavodia/Statistical-Rethinking-
with-P...](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-
and-PyMC3)

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singhrac
As a frequent user of Edward ( _big_ fan), I'm not sure I should be crazy
about this - while it probably means that it'll get more frequent updates and
love, I liked the researchy vibe that it had and its strong ties to sampling-
based methods, etc. which might be lost in a move into contrib.

That being said, Dustin is probably working closely with Google people to do
this properly, so it'll probably turn out ok in the end.

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martingoodson
The statistician Bob Carpenter has commented that '...I just don’t think the
Edward devs care much about doing practical MCMC.'[1]

Anyone got experience of using Edward for serious inference using MCMC?

[1] [http://andrewgelman.com/2017/05/31/compare-stan-
pymc3-edward...](http://andrewgelman.com/2017/05/31/compare-stan-pymc3-edward-
hello-world/)

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ckdarby
Came here thinking Edward Snowden was joining the Tensorflow team.

