Timeline for Probability the next draw from a distribution is greater than some number given a previous draw
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 16, 2021 at 16:47 | comment | added | whuber♦ | This claim that a "Bayesian setting" is needed is incorrect. Question (2) is fully answerable; question (1) has an answer that depends on $a.$ | |
| Feb 4, 2016 at 14:47 | comment | added | user4422 | if your prior is conjugate (as suggested in the link above) then you have an analytical solution for the posterior density and you can use it to compute the probability you need. | |
| Feb 4, 2016 at 0:50 | comment | added | sundance | Okay, I suppose you could just guess that it is the mean of the distribution (.5). After obtaining these priors, what is the next step? How do you update the distribution with the realized signals and then compute the function at the end of the post? | |
| Feb 3, 2016 at 21:25 | comment | added | user4422 | if you do not know a, then either you make a guess about its value (which means that you put a dirac delta prior on it) or you properly model the uncertainty by putting a proper prior, with a non-zero dispersion. Otherwise, what is the meaning of unknown? | |
| Feb 3, 2016 at 16:34 | comment | added | sundance | In (1), $a$ can be any number on the interval $[0,1]$, and is chosen by a person. How can you form a prior distribution if there is no randomness? In (2), the prior is given: $b$ and $c$ are drawn from $U(0,1)$, so that would be the prior. | |
| Feb 3, 2016 at 10:21 | history | answered | user4422 | CC BY-SA 3.0 |