Timeline for Likelihood convexification
Current License: CC BY-SA 3.0
13 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 1, 2015 at 9:04 | vote | accept | rhombidodecahedron | ||
| S Dec 1, 2015 at 6:28 | history | bounty ended | Tomas | ||
| S Dec 1, 2015 at 6:28 | history | notice removed | Tomas | ||
| Nov 30, 2015 at 18:35 | answer | added | jbowman | timeline score: 3 | |
| Nov 26, 2015 at 13:40 | comment | added | Guillaume Dehaene | rhombidodecahedron: you say you find a lot of different local minima of your objective function, but have you checked whether all local-minima are close to one another in space ? whether their likelihoods are very different ? Even if L doesn't have a reason to be nice, it might not be that horrible | |
| Nov 25, 2015 at 20:18 | comment | added | jbowman | Roughly how high dimensional is it? "Very" differs from person to person, after all. | |
| Nov 25, 2015 at 20:00 | comment | added | dave fournier | I would like to see the function (and probably the data needed to run it). | |
| Nov 25, 2015 at 19:34 | comment | added | whuber♦ | If you could find a way to make a function convex (while preserving essential properties such as the location of its global maximum), then--because finding its global maximum would then be straightforward--you would have performed a feat even more difficult than optimizing it. Doesn't that make it obvious there cannot possibly be a "silver bullet"? | |
| Nov 25, 2015 at 19:32 | history | tweeted | twitter.com/StackStats/status/669599673116684289 | ||
| Nov 25, 2015 at 18:57 | comment | added | kjetil b halvorsen♦ | I doubt that much can be sdaid at this level of abstraction, so you need to give more details. | |
| S Nov 25, 2015 at 18:52 | history | bounty started | Tomas | ||
| S Nov 25, 2015 at 18:52 | history | notice added | Tomas | Draw attention | |
| Feb 17, 2015 at 23:18 | history | asked | rhombidodecahedron | CC BY-SA 3.0 |