Timeline for What is the intuition behind beta distribution?
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 23, 2023 at 4:36 | history | edited | User1865345 | CC BY-SA 4.0 | added 1 character in body |
| Nov 15, 2013 at 21:37 | history | edited | Raffael | CC BY-SA 3.0 | trying to clarify what the bar chart represents |
| Nov 15, 2013 at 21:33 | comment | added | Raffael | That is a good point! But I am not sure how to rephrase it propperly. If I would just plot the histogram then, of course, you wouldn't see much of the density given the magnitude of it. So yes, the histogram is actually I guess not just scaled down but actually the (estimated) density of the original histogram. Given the number of runs I could also figure out a factor and scale it down linearly but it would look almost exactly the same PLUS what I (actually) want to compare is the density of beta with the density of the result of the simulation (the density of the original histogram). | |
| Nov 15, 2013 at 21:12 | comment | added | whuber♦ | Thank you for your contribution! I am puzzled about something, though: although the histogram legend states they show beta densities, you appear to claim these also describe the outcomes of binomial simulations ("how often it happend in a simulation"). But the two are different things, even though they happen to appear fairly close in the illustration. (That's a consequence of the near-normality of the Beta with large parameters and the Central Limit theorem for Binomial distributions.) | |
| Nov 15, 2013 at 20:17 | history | answered | Raffael | CC BY-SA 3.0 |