Timeline for Why is the Kalman Filter a specific case of a (dynamic) Bayesian network?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 31, 2016 at 18:35 | vote | accept | Chill2Macht | ||
| Aug 31, 2016 at 6:22 | answer | added | Juho Kokkala | timeline score: 10 | |
| Aug 31, 2016 at 5:03 | comment | added | Juho Kokkala | The remark (and the commenters above) is about the discrete-time Kalman filter, not about the continuous-observation Kalman-Bucy filter (check, e.g. ,the Wikipedia article about Kalman filters). | |
| Aug 31, 2016 at 4:59 | comment | added | GeoMatt22 | Note that in Kalman filters the (hidden) state and (measured) observations are typically sampled at discrete time intervals. Most of their apparent complexity is due to the matrix calculations involved. | |
| Aug 31, 2016 at 4:54 | comment | added | GeoMatt22 | Lesson 2 of this free* Udacity course may help to de-mystify Kalman filters. If I remember correctly, it gives the practical big picture without bogging down in linear algebra. (*free at the time of this writing, at least) | |
| Aug 31, 2016 at 4:19 | history | asked | Chill2Macht | CC BY-SA 3.0 |