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> All that probability shorthand can be unambiguously translated to formal definitions quite easily. But doing so would be analogous to writing a complex program in assembly - doable (and defined pretty much by the very fact that this is doable) but not very productive (and thus not worth doing unless you are debugging or something).

Actually I kind of disagree here.

With R or Haskell you can easily work directly with probability densities learned from data. One frequently uses the exact Bayes' rule expression with P(X), P(Y), and P(X|Y) all being known functions to get P(Y|X).

See for example functions like ecdf, which takes in an N vector of points on a 1D line and returns an actual function, namely the empirical cumulative density.

http://stat.ethz.ch/R-manual/R-patched/library/stats/html/ec...

Can be very handy when you want empirical quantiles (e.g. "what percentage of the time do I expect to see 12000 hits in a day, given this single column with the hits for each of the last 200 days").



I don't really understand why what you said disagrees with what your parent said?


Perhaps I read too quickly -- when he said:

> All that probability shorthand can be unambiguously translated to formal definitions quite easily. But doing so would be analogous to writing a complex program in assembly

One possible interpretation (probably, in retrospect, the right one) is that he meant that Whitehead/Russell style axiomatization of probability was in theory possible, but would not be of much value.

I read it initially (likely wrongly in retrospect) as saying that translating the equations into an unambiguous formal computer readable definition would be intractable and/or only of theoretical interest.




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