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, [email protected] 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!

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