2 ms·
i think robotics is probably an easier example to think about. if you have a sensor who's normal operation has a well defined gaussian, but every 2-4 seconds a
by gmadsen 4y ago
i think robotics is probably an easier example to think about. if you have a sensor who's normal operation has a well defined gaussian, but every 2-4 seconds a spike in the data stream is seen for a millisecond due to any variety of reasons, you treat the spike as an outlier and dont let it ruin the gaussian that is actually governing your system, the spike is obviously not part of the nominal distribution, and it wouldn't be of any usable value to think of the system as a mixture.
- civilized 4y agoThis is treating the system as a mixture. It's just immediately getting rid of the component of the mixture you don't want. If you can do so reliably, you never have to think about it as a mixture again.
- kqr 4y agoIt's incredibly valuable to think of the system as a mixture, and work on both components of the problem in parallel. (Both increasing precision to narrow the width of the gaussian, and removing the defective interaction that causes the spikes.)