Timeline for Why is everything based on likelihoods even though likelihoods are so small?
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 26 at 14:06 | comment | added | Uk rain troll | Unfortunately I could only accept one answer :( Thank you so much ... I wrote this question about simulations just like you...can you please see it? stats.stackexchange.com/questions/641165/… | |
| Feb 18 at 22:41 | comment | added | Michael Hardy | You do not typically work with the logarithm of the likelihood function when multplying the prior by the likelihood function and then normailizing, to get the posterior distribution. | |
| Feb 18 at 5:39 | comment | added | Michael Lew | I would also add that the convenience of scaling the likelihood function to have unit maximum is possible because the likelihoods are only used as ratios. It is also worth noting that you have only dealt with the mean parameter, whereas the question included variation of both the mean and spread parameters. (I only mention this because the OP seems to be new to likelihoods.) | |
| Feb 18 at 5:35 | comment | added | Michael Lew | I would say that the mathematical convenience of using log likelihood functions are more than counterbalanced by the un-intuitiveness introduced by the log scale. In the figures you supplied the support by the data of means near 5 is much more easily seen in the linear likelihood graph. | |
| Feb 18 at 5:24 | history | answered | Durden | CC BY-SA 4.0 |