4 ms·
In the example given, the author says that the odds of a given page having a typo is less than 3/20. Sure, but if we don't want a range, but an exact number? Th
by whack 8y ago
In the example given, the author says that the odds of a given page having a typo is less than 3/20. Sure, but if we don't want a range, but an exact number? That sounds like a more interesting challenge to me.
Formal statement:
- You have observed N events, with 0 occurrences of X
- Someone wants to make a bet with you about the likelihood of X happening
- Once you've quoted a number, your counter-party then has the option of making an even bet about whether the actual likelihood is greater than or less than your prediction
Eg: If you predict 3/N using a 95% confidence interval, then 95% of the time, the actual likelihood will be less than 3/N. Your counterparty will then win the bet 95% of the time, simply by predicting it to be lower.
Your ideal strategy would be to quote a likelihood which is over/under 50% of the time, not 95%.
Ie, you want to pick E such that 50% of the time, it matches the observation you've made (no occurrences), and 50% of the time it does not.
E^N = 0.5
N log E = log 0.5
log E == log 0.5 / N
E = 0.5^(1/N)
For the example given, that comes out to 0.966. Ie, there's a 96.6% chance of no typos in a given page. Across 20 pages, this comes out to 0.966^20 => 50% chance of no typos. If your goal is to quote the single best estimate which can hold up well in a betting market, I believe this would be the ideal strategy
- olooney 8y agohttps://en.m.wikipedia.org/wiki/Rule_of_succession https://en.m.wikipedia.org/wiki/Rule_of_succession https://en.m.wikipedia.org/wiki/Sunrise_problem https://en.m.wikipedia.org/wiki/Sunrise_problem
- AstralStorm 8y agoThis presumes conditional independency - violations of which are common. Instead, you get to estimate the dependency between each observation as in advanced variants of Bayes chain rule. Ultimately, some place of the estimator will contain an assumption giving only bounded optimality.
- gweinberg 8y agoI don't think you can do this. You have a reasonably good upper bound of the probability, but you don't have any justification for putting any lower bound other than zero on the probability. In particular, if you're considering the probability of a catastrophic event, it's probably better to find some other rationale for estimating the probability than just saying 'it has never happened before'.
- carlmr 8y agoExactly, the maximum likelihood estimator given your data is 0. You can just have a looser or stricter upper bound, depending on your requirements. However without a better model of your distribution and/or better priors (experience), you won't get a tighter estimate.