Timeline for Generating causally dependent random variables
Current License: CC BY-SA 3.0
14 events
| when toggle format | what | by | license | comment | |
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| S Oct 24, 2013 at 15:36 | history | bounty ended | CommunityBot | ||
| S Oct 24, 2013 at 15:36 | history | notice removed | CommunityBot | ||
| Oct 16, 2013 at 20:14 | answer | added | AsymLabs | timeline score: 0 | |
| Oct 16, 2013 at 17:35 | answer | added | user31264 | timeline score: 1 | |
| S Oct 16, 2013 at 13:38 | history | bounty started | sebastian | ||
| S Oct 16, 2013 at 13:38 | history | notice added | sebastian | Draw attention | |
| Oct 14, 2013 at 15:03 | history | edited | sebastian | CC BY-SA 3.0 |
added 283 characters in body
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| Oct 14, 2013 at 14:14 | comment | added | whuber♦ | Your comments indicate you are assuming that $\mathbf{a}$ and $\mathbf{v}$ are independent. That cannot possibly be, because there are physical limitations to speeds: that means many accelerations will not be experienced at the most extreme speeds. However, it's not easy to provide more detailed advice because you haven't articulated what you're trying to accomplish; instead, you have described an approach to solving an unstated problem. Why don't you change this question and ask instead about the problem you need to solve rather than how to implement a solution that looks invalid? | |
| Oct 14, 2013 at 6:28 | comment | added | sebastian | For completeness: the data originates from gps-logging. I have a set of logged trips in cars, which log speed with 1Hz. So their's a pair of $v$ and $a$ for every datapoint. These are filled into the histogram. | |
| Oct 14, 2013 at 6:17 | comment | added | sebastian | Well, to me its kind of obvious, because the $\bf{a}$ distributions are pretty much symmetric around zero. So when generate the $a_i$ there's no dependency on $v$. When the current $v$ is at the upper edge of the marginal $\bf{v}$ distribution, you'd assume that there should be a bias towards negative $a_i$. "draw values" refers to: take the 1-dim probability distrbution, built the cumulative distribution, throw a random number $r$ between 0 and 1, find the $x$ where the cum. distribution has the value $r$. This $x$ is my "drawn value" | |
| Oct 13, 2013 at 0:20 | history | tweeted | twitter.com/#!/StackStats/status/389183745468952576 | ||
| Oct 10, 2013 at 16:44 | comment | added | whuber♦ | An interesting question. However, the second "obviously" (about not respecting the marginal distribution) is not at all clear to me. Why is it obvious? The distribution of $(v,a)$, as reflected by your "two-dimensional histogram," depends on how you have sampled these variables; I wonder whether this might explain possible differences. What kind of data are represented by this histogram and how exactly do you "draw values" from it? | |
| Oct 10, 2013 at 16:42 | review | First posts | |||
| Oct 10, 2013 at 17:09 | |||||
| Oct 10, 2013 at 16:26 | history | asked | sebastian | CC BY-SA 3.0 |