
Probabilistic programming from scratch - GrantCuster
http://blog.fastforwardlabs.com/2017/07/06/probabilistic-programming-from-scratch.html
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robterrin
Cool! Thanks for sharing.

I would also like to add, Stan is not that hard. Conceptually it's a bit of a
jump, especially if you're coming from comp sci not statistics, but I think
it's really worth it. Here are some helpful links:

Stan website: [http://mc-stan.org/](http://mc-stan.org/)

A primer on Stan:
[http://www.stat.columbia.edu/~gelman/research/published/stan...](http://www.stat.columbia.edu/~gelman/research/published/stan_jebs_2.pdf)

The Python interface PyStan documentation:
[https://pystan.readthedocs.io/en/latest/](https://pystan.readthedocs.io/en/latest/)

A case using Stan in a real startup's product:
[https://www.smartly.io/blog/tutorial-how-we-productized-
baye...](https://www.smartly.io/blog/tutorial-how-we-productized-bayesian-
revenue-estimation-with-stan)

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canjobear
Another good one: [http://dippl.org/](http://dippl.org/)

~~~
ice109
dippl would be a good name for a startup

~~~
mpweiher
And it should headed up by a Dipl. Ing. Or a Dipl. Inf.

[https://en.wikipedia.org/wiki/Engineer%27s_degree#Germany_an...](https://en.wikipedia.org/wiki/Engineer%27s_degree#Germany_and_Austria)

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Eridrus
A similar package is Edward: [https://github.com/blei-
lab/edward/blob/master/README.md](https://github.com/blei-
lab/edward/blob/master/README.md) Which was built on TensorFlow, so it has
neural networks goodies, but more importantly is GPU-accelerated! PyMC3 is on
the way to using Theano but wasn't done last time I checked.

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Ranlot
This reminds me of this discussion about AB testing, power statistics &
significance: [https://p-value-convergence.herokuapp.com/](https://p-value-
convergence.herokuapp.com/)

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agumonkey
Thanks, got me interested in the subject.

