network profile
| bio | website | oddskool.net |
|---|---|---|
| location | France | |
| age | 32 | |
| visits | member for | 1 year, 7 months |
| seen | May 6 at 13:45 | |
| stats | profile views | 4 |
part-time statistician, software engineer
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Apr 16 |
comment |
How to uniformly project a hash to a fixed number of buckets Thanks for insisting on that point, I re-run my counting on the hashes' first letter and it seems indeed ~uniformly distributed : {'a': 789, 'c': 769, 'b': 755, 'e': 730, 'd': 804, 'f': 749, '1': 716, '0': 758, '3': 734, '2': 735, '5': 787, '4': 756, '7': 771, '6': 721, '9': 764, '8': 765}. Therefore my question is more or less answered as I just need to project this 16-states random generator to a 100-states space, which can be done using the first 2 letters of the hash to generate an integer of range [0,16+16*16] and modulo it to 100. Mind if I answer my own question ;) ? |
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Apr 15 |
comment |
How to adjust average rating for sample size on rating systems with more than two categories? Right, more certain estimates should be given more weight. I suspect there are means to render this notion in the bayesian framework by playing with the risk functions. From an application perspective, you can naively incorporate such a notion by discarding (or down-weighting) items with less than a pre-defined number of reviews. You can also incorporate other elements like date of reviews, reviewers profiles etc. At a certain point you might want to switch to a Learning to Rank approach to combine all those elements. |
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Apr 13 |
comment |
How to uniformly project a hash to a fixed number of buckets I doubt there is a bug in the hashing algorithm itself. But I suspect the characters of the hex digest not to be strictly uniform and independently distributed. |
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Jan 5 |
comment |
How to compute median in an online fashion? @Andy W, thanks too, those pointers are very good indeed ! Too bad the system didn't manage to find'em when I wrote the question... |
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Jan 5 |
comment |
How to compute median in an online fashion? @ocram : yes, very good entry point for my question, thanks ! |
