Which thread was this, if you don't mind?
It's a good line, what is the context / source for that quote?
As my sibling poster indicates, the SO survey is a convenience sample not a census of coders. If you look at the 2016 survey, the 0-5 years experience cohort is larger (50.3%) than than it is in 2019, so I am not sure…
There's also auto scikit-learn https://github.com/automl/auto-sklearn if you haven't already come across that.
Lise Getoor have a nice talk at NIPS in 2017 on a what I think is a formalism, Probabilistic Soft Logic, that her group developed: https://youtu.be/t4k5LKCpboc . Definitely an interesting direction of research.
> Floating point numbers are the optimal minimum message length method of representing reals with an improper Jeffery's prior distribution. Do you have a link to a proof or discussion of this? I haven't heard this…
I think Jeffreys, Wald and Savage had all started on advocating Bayesian statistics/probability before Jaynes.
Which thread was this, if you don't mind?
It's a good line, what is the context / source for that quote?
As my sibling poster indicates, the SO survey is a convenience sample not a census of coders. If you look at the 2016 survey, the 0-5 years experience cohort is larger (50.3%) than than it is in 2019, so I am not sure…
There's also auto scikit-learn https://github.com/automl/auto-sklearn if you haven't already come across that.
Lise Getoor have a nice talk at NIPS in 2017 on a what I think is a formalism, Probabilistic Soft Logic, that her group developed: https://youtu.be/t4k5LKCpboc . Definitely an interesting direction of research.
> Floating point numbers are the optimal minimum message length method of representing reals with an improper Jeffery's prior distribution. Do you have a link to a proof or discussion of this? I haven't heard this…
I think Jeffreys, Wald and Savage had all started on advocating Bayesian statistics/probability before Jaynes.