3 ms·
Strikes me mainly as a trendy recasting of the statistical concept of a "sufficient statistic" (http://en.wikipedia.org/wiki/Sufficient_statistic http://en.wiki
by mturmon 14y ago
Strikes me mainly as a trendy recasting of the statistical concept of a "sufficient statistic" (http://en.wikipedia.org/wiki/Sufficient_statistic http://en.wikipedia.org/wiki/Sufficient_statistic)
- deleted 14y ago[deleted]
- bazzargh 14y agoWhich would be to miss the point that monoids are the general principal of which sufficient statistics are a specific application. Also, not sure mathematicians wore skinny jeans in 1904.
- elliptic 14y agoSurely the burden is squarely on the author to show what the generalization (in this context) offers that the specific concept of sufficient statistics does not? In other words, how else can a statistician or ML expert use this foreign "monoid" concept to improve or better understand parameter estimation?
- bazzargh 14y ago"show what the generalization (in this context) offers that the specific concept ... does not" What does group theory tell me in the context of 1+1 that knowing the answer is 2 doesn't? Generalizations are useful because they apply to more than one context. That said; the interesting connection between two areas of mathematics is what I took from the article, but I'd agree that his title (well, subtitle) is way overblown - his argument for "Why ML experts should care" seems to boil down to speed, but the Haskell statistics package itself contains faster algorithms that are marked unsafe, and he hasn't demonstrated safety.