I had to do a double take when I saw Bayes's theorem on that illustration - "wait a second, did I read that domain name correctly"?
Even more surprising is that they would use the terminology of prior and posterior probabilities and the like, applied correctly. It gives one the impression that the author knows what he is talking about.
I don't know how motivating this article would be to someone without training in probability theory, though. I mean, it's all well and good to be factually correct, but showering people outside of the field with technical terms and "weird" claims can, I think, be very counterproductive.
Concerning the theorem itself, no effort was actually made to at least rephrase it in a more friendly way. It's not enough to just poke fun at yourself with "(trumpets sound here)". Many people, even with formal mathematical training do not intuitively understand why it is true, and without that, applying it for probability calculations would feel like hollow and mindless mechanical work.
To that effect, I also recommend Eliezer's explanation, found at [0]. I must admit that I kind of glossed over it, because I had the correct intuition regardless of not formally knowing the theorem (no idea how to reproduce that, unfortunately, because it would be a pretty good tool), but even so, I found it both funny and informative. And as a bonus, it doesn't assume that you have any rigorous training in mathematics (a common misconception among mathematical explanations).
> Even more surprising is that they would use the terminology of prior and posterior probabilities and the like, applied correctly. It gives one the impression that the author knows what he is talking about.
Well, the author of the review is John Allen Paulos, a mathematics professor.
Understanding why Bayes' theorem is true isn't the hardest part. P(A and B) is both P(A) P(B|A) and P(B) P(A|B), and the result follows. I find this derivation easier to remember than the usual formula, by the way.
Also, while Bayes' Theorem follows from probability axioms, Bayes' Rule for updating beliefs in light of new evidence (which has the same formulation) is justified by Cox's Theorem:
Apart from the obvious (buy a subscription), you can open the link in an incognito/private-browsing session, since the NYT monthly page limit seems to be based on cookies.
Even more surprising is that they would use the terminology of prior and posterior probabilities and the like, applied correctly. It gives one the impression that the author knows what he is talking about.
I don't know how motivating this article would be to someone without training in probability theory, though. I mean, it's all well and good to be factually correct, but showering people outside of the field with technical terms and "weird" claims can, I think, be very counterproductive.
Concerning the theorem itself, no effort was actually made to at least rephrase it in a more friendly way. It's not enough to just poke fun at yourself with "(trumpets sound here)". Many people, even with formal mathematical training do not intuitively understand why it is true, and without that, applying it for probability calculations would feel like hollow and mindless mechanical work.
To that effect, I also recommend Eliezer's explanation, found at [0]. I must admit that I kind of glossed over it, because I had the correct intuition regardless of not formally knowing the theorem (no idea how to reproduce that, unfortunately, because it would be a pretty good tool), but even so, I found it both funny and informative. And as a bonus, it doesn't assume that you have any rigorous training in mathematics (a common misconception among mathematical explanations).
[0] : http://yudkowsky.net/rational/bayes