Timeline for How can we incorporating uncertainty about our data into Bayesian inference?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Jun 8, 2021 at 10:51 | comment | added | Chris | Great. Thanks for the update @mef, perfectly understandable now. Will check it out. | |
| Jun 8, 2021 at 10:51 | vote | accept | Chris | ||
| Jun 7, 2021 at 16:34 | history | edited | mef | CC BY-SA 4.0 |
Add detail for simulation and remove introductory material made unnecessary by OP's changes.
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| Jun 7, 2021 at 14:39 | comment | added | Chris | I updated my original question below, such that it notationally conforms with your proposed solution (choice of parameter), and already states the form of the vanilla Beta distribution, making it unecessary for you to add this detail. | |
| Jun 7, 2021 at 14:12 | comment | added | Chris | Thanks for the second approach, which makes a lot of sense. I just need some clarification on the approximation: do I assume correctly that you suggest to sample $R$-times $s$ from $p(s|\pi)$? If so, how do I calculate an individual sample for $s \in [0,n]$ with $n \approx 3000?$ | |
| Jun 7, 2021 at 12:55 | history | answered | mef | CC BY-SA 4.0 |