Timeline for Ornstein-Uhlenbeck vs. Random Normal?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Jun 6, 2013 at 18:07 | comment | added | Cam.Davidson.Pilon | Time averaging won't eliminate all serial correlations. What exactly is the process measuring? If the process is just noise, then sure, use normal generations, but if the next value of the process depends on the previous (for example, how a cumulative sum at time $t$ depends on the cumulative sum at time $t-1$), I would use something else. | |
| Jun 6, 2013 at 18:01 | comment | added | Blink | The simulation is a multiphase mixture (sand + air). I am varying the coefficient of restitution of the sand throughout the simulation (every x seconds). Because I don't know the exact value of the coefficient, I am using a distribution (which I want to sample from). The results will be time averaged, which should eliminate all serial correlations. | |
| Jun 6, 2013 at 17:52 | comment | added | Cam.Davidson.Pilon | What quantities are you varying? | |
| Jun 6, 2013 at 17:50 | vote | accept | Blink | ||
| Jun 6, 2013 at 17:50 | comment | added | Blink | My application involves varying quantities throughout a simulation that are not normally varied (but have a certain probability distribution) and seeing how the output varies. Based on what you have said, I don't believe I actually need an O-U process as I will be time-averaging over all the changes (eliminating any serial correlations). Does that sound correct to you? | |
| Jun 6, 2013 at 17:45 | vote | accept | Blink | ||
| Jun 6, 2013 at 17:50 | |||||
| Jun 6, 2013 at 17:33 | comment | added | Cam.Davidson.Pilon | this is the first time in over a year I've actually found a use for my web-app! | |
| Jun 6, 2013 at 17:31 | history | answered | Cam.Davidson.Pilon | CC BY-SA 3.0 |