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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 codeb

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 9y ago
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 9y ago
  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 9y ago
    Absolutely! Great stuff.
splike 9y ago
I've only seen Edward for the first time today, and it looks quite nice. Could you (or anyone else) comment on why would you choose Edward over something PyMC3? How does it compare?
- cshenton 9y ago
  I had the same question when I was first choosing libraries for my current project! So I put together this comparison of PyMC3, Edward, and Stan. It's jut doing bayesian inference on the location and scale of data sampled from a normal distribution. Super simple, but highlights the differences between the libraries. https://github.com/cshenton/normal-comparison https://github.com/cshenton/normal-comparison Having worked with Edward, the LOC difference is deceptive. Edward has really solid abstract base classes, so developing on it is much more expressive for larger projects. I unequivocally recommend using Edward.
  - splike 9y ago
    That's useful, thanks!

nextos 9y ago

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 9y ago
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 9y ago
  Absolutely! Great stuff.

splike 9y ago

I've only seen Edward for the first time today, and it looks quite nice. Could you (or anyone else) comment on why would you choose Edward over something PyMC3? How does it compare?

cshenton 9y ago
I had the same question when I was first choosing libraries for my current project! So I put together this comparison of PyMC3, Edward, and Stan. It's jut doing bayesian inference on the location and scale of data sampled from a normal distribution. Super simple, but highlights the differences between the libraries. https://github.com/cshenton/normal-comparison https://github.com/cshenton/normal-comparison Having worked with Edward, the LOC difference is deceptive. Edward has really solid abstract base classes, so developing on it is much more expressive for larger projects. I unequivocally recommend using Edward.
- splike 9y ago
  That's useful, thanks!