Evidence/data for the bright future of Probabilistic Programming

[hugobowne]

Both @ericmjl and I are firm believers that Probabilistic Programming has a bright and huge future.

I know other people believe the same. @springcoil has said toe me previously that "PP is the new deep learning" and I understand that @twiecki feels similarly.

What I'd like to do here is amass evidence of the bright future of PP and why we think it will garner increasing adoption.

A few things I've thought of

• FB uses Bayesian techniques and PP, such as Prophet
• PyMC3 has ~5K stars on github: https://github.com/pymc-devs/pymc3
• Bayesian quant methods Quantopian (see here, for example)
• Nate Silver using Bayesian methods for 538
• FFLabs (usually ahead of the curve) had a WP on PPL in 2017
• Growth of academic conferences: PROBPROG2019 and 2020, whole conferences dedicated to the study and application of probabilistic programming languages.
• In 2013, O’Reilly itself published a blog post introducing probabilistic programming.

I appreciate this is very limited!

What other evidence/data is there for the future of PPL?

Note: @ericmjl and I are currently drafting a book proposal for O'Reilly, which motivated this question.

Tagging @fonnesbeck, @ericmjl, @betanalpha, @FrizzleFry, @springcoil, @twiecki, @justinbois, @AllenDowney as you all may have thoughts here. Do feel free to tag anybody else you think may have ideas.

thanks!

[springcoil]

I'd add that explanations of models matter more and more in professional data science. Covid examples are good examples of this.

…

On Wed, 22 Apr 2020, 07:49 Hugo Bowne-Anderson, ***@***.***> wrote: Both @ericmjl <https://github.com/ericmjl> and I are firm believers that Probabilistic Programming has a bright and huge future. I know other people believe the same. @springcoil <https://github.com/springcoil> has said toe me previously that "PP is the new deep learning" and I understand that @twiecki <https://github.com/twiecki> feels similarly. What I'd like to do here is amass evidence of the bright future of PP and why we think it will garner increasing adoption. A few things I've thought of - FB uses Bayesian techniques and PP, such as Prophet <https://facebook.github.io/prophet/> - PyMC3 has ~5K stars on github: https://github.com/pymc-devs/pymc3 - Bayesian quant methods Quantopian <https://www.quantopian.com/> (see here <https://blog.fastforwardlabs.com/2017/01/11/thomas-wiecki-on-probabilistic-programming-with.html>, for example) - Nate Silver using Bayesian methods for 538 - FFLabs (usually ahead of the curve) had a WP on PPL in 2017 <https://blog.fastforwardlabs.com/2017/01/18/new-research-on-probabilistic-programming.html> - Growth of academic conferences: PROBPROG2019 and 2020, whole conferences dedicated to the study and application of probabilistic programming languages. - In 2013, O’Reilly itself published a blog post introducing probabilistic programming <https://www.oreilly.com/content/probabilistic-programming/>. I appreciate this is very limited! What other evidence/data is there for the future of PPL? Note: @ericmjl <https://github.com/ericmjl> and I are currently drafting a book proposal for O'Reilly, which motivated this question. Tagging @fonnesbeck <https://github.com/fonnesbeck>, @ericmjl <https://github.com/ericmjl>, @betanalpha <https://github.com/betanalpha>, @FrizzleFry <https://github.com/FrizzleFry>, @springcoil <https://github.com/springcoil>, @twiecki <https://github.com/twiecki>, @justinbois <https://github.com/justinbois>, @AllenDowney <https://github.com/AllenDowney> as you all may have thoughts here. Do feel free to tag anybody else you think may have ideas. thanks! — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#91>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAHQHCFBETXX3INO233JOILRN2APHANCNFSM4MN3OBIA> .