Timeline for Does every commonly used loss function have an interpretation as maximum likelihood estimation for some likelihood?
Current License: CC BY-SA 4.0
12 events
| when toggle format | what | by | license | comment | |
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| S May 30, 2023 at 19:07 | history | bounty ended | Dave | ||
| S May 30, 2023 at 19:07 | history | notice removed | Dave | ||
| May 24, 2023 at 9:32 | answer | added | Wilbur | timeline score: 1 | |
| May 23, 2023 at 21:40 | comment | added | seanv507 | hastie.su.domains/Papers/ESLII.pdf page see section 10.5 page 346 page, points out exponential loss is not a log likelihood. | |
| May 23, 2023 at 19:24 | comment | added | Durden | Can't offer a full answer, but for an example with regularization consider the fact that LASSO is essentially Bayesian regression with a Laplace prior. | |
| S May 23, 2023 at 18:46 | history | bounty started | Dave | ||
| S May 23, 2023 at 18:46 | history | notice added | Dave | Draw attention | |
| May 23, 2023 at 18:44 | history | edited | Dave | CC BY-SA 4.0 |
added 371 characters in body; edited tags; edited title
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| May 4, 2019 at 14:28 | comment | added | kjetil b halvorsen♦ | Can you give (or point to) a list of these "commonly used loss functions"? | |
| Jan 4, 2019 at 22:14 | comment | added | knrumsey | I agree that the question is too vague currently to receive a helpful answer. I will comment that minimizing $L_1$ loss is equivalent to maximizing the likelihood under the Laplacian (Double exponential) distribution. | |
| Jan 4, 2019 at 20:55 | comment | added | Xi'an | You need to make the question more formal and less vague, otherwise the answer is going to be an unhelpful yes. | |
| Jan 4, 2019 at 20:35 | history | asked | George | CC BY-SA 4.0 |