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I read the article, went "The ^&#$?" and came here to post this, more or less. It's amazing the $&#! people will get up to when they don't know Bayesian statis
by Eliezer 15y ago
I read the article, went "The ^&#$?" and came here to post this, more or less. It's amazing the $&#! people will get up to when they don't know Bayesian statistics.
- moultano 15y agoThat really isn't fair to what this is doing. If a bayesian wanted to lower bound the distribution, they would be using exactly the same formula, but with an explicit prior. The typical bayesian solution to this of the dirichlet prior has a significantly different convergence rate, 1/n vs 1/sqrt(n) so the resulting ordering is substantially different as well, and I would argue worse for this application. The method of setting a pessimistic prior cares much less about the number of votes for a popular item than does the method of lower bounding the distribution.
- Eliezer 15y agoIf you think most comments are bad, and you have an explicit prior that reflects this, then your ranking will update to "Hey, the comment was actually good!" at the optimal speed - no more, no less - given the rate of incoming ratings. Why would any other rate of updating be better?
- moultano 15y agoBecause there's a high variance on your estimate, so in a lot of circumstances just by variance you'll show users bad comments on top most of the time, rather than showing them the good comment that you are certain of. In this context, the pessimistic prior is more of a hack than the confidence interval, because your mental model isn't that most comments suck. Your mental model is that most of them are ok. The pessimistic prior is just there to say, if 1000 people confirm that a comment is ok, we should show that over a comment for which we don't know whether it's ok or not yet, and this reasoning is much better modeled by a lower bound.