Timeline for Criteria for choosing a mean function for a GP
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 3, 2021 at 17:03 | comment | added | Ken Grimes | "... then you need to use simulation (MCMC) if you want to perform "exact" Bayesian inference." <- What means? Is it impossible to use maximum likelihood to optimize the hyperparameters?? | |
| Jun 11, 2020 at 14:32 | history | edited | CommunityBot |
Commonmark migration
|
|
| Dec 30, 2019 at 21:23 | comment | added | tea_pea | Does anyone have references for when these techniques have been used in the literature/practise (particularly 'mean function is a linear model')? | |
| Jul 29, 2019 at 8:35 | comment | added | An old man in the sea. | DeltaIV I was rereading your answer, and I found this sentence interesting: «(...) or length-scales which best fit the training data are very small with respect to the "diameter" of the training set.» It was about the GP being mean reverting to an uninteresting mean. What mathematical criteria can we use to ascertain whether the 'length-scales' parameters are too small with respect to the data diameter? | |
| Nov 18, 2018 at 22:28 | vote | accept | An old man in the sea. | ||
| Nov 7, 2018 at 20:17 | history | edited | DeltaIV | CC BY-SA 4.0 |
added 17 characters in body
|
| Nov 7, 2018 at 19:27 | history | edited | DeltaIV | CC BY-SA 4.0 |
deleted 1 character in body
|
| Nov 7, 2018 at 19:05 | history | edited | DeltaIV | CC BY-SA 4.0 |
deleted 1 character in body
|
| Nov 7, 2018 at 19:00 | history | answered | DeltaIV | CC BY-SA 4.0 |