Timeline for Coalescing random walks: a bound for the full coalescence time?
Current License: CC BY-SA 2.5
6 events
| when toggle format | what | by | license | comment | |
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| Oct 21, 2013 at 17:14 | comment | added | petrelharp | Comment: very nice. Just what I was looking for. =) | |
| Sep 22, 2010 at 11:52 | vote | accept | Roberto Imbuzeiro Oliveira | ||
| Sep 22, 2010 at 11:52 | |||||
| Sep 22, 2010 at 11:52 | comment | added | Roberto Imbuzeiro Oliveira | A continuous-time Markov chain over a finite set $S$ is a process such that, for all distinct $x,y\in S$, the chance of being at $y$ at time $t+\epsilon$, given that the state at time $t$ is $x$, $q(x,y)\epsilon + o(\epsilon)$, where $q(x,y)$ are the so-called transition rates. This moves by "jumps" only. | |
| Sep 22, 2010 at 2:45 | history | edited | Roberto Imbuzeiro Oliveira | CC BY-SA 2.5 |
Changed file link to arxiv abstract page.
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| Sep 4, 2010 at 3:55 | comment | added | sleepless in beantown | Question: why are you using a continuous time random walk on a graph instead of a discrete time random walk? What distribution do you use for the distance which a walker will travel during each of its steps? I can see how to define a continuous time random walk on $\mathbb{R}^n$ where you can have it move a distance $d$ taken from a normal distribution $\overbar(x)+\sigma$ for each time step, or have it move 1−unit distance for a normal−distributed timestep $\overbar(t)+\sigma_{t}$. Thank you for putting the pdf file on your server. (I have only read the first 2 pages -> my 1st question) | |
| Sep 4, 2010 at 3:32 | history | answered | Roberto Imbuzeiro Oliveira | CC BY-SA 2.5 |