3 ms·
In robotics, particularly in bayesian filtering (KFs and so on), I find the idea of a "prior" solid and I don't see any frequentist alternatives. Your prior is
by jefft255 7y ago
In robotics, particularly in bayesian filtering (KFs and so on), I find the idea of a "prior" solid and I don't see any frequentist alternatives. Your prior is easy to understand: whatever your posterior for your state was at the previous timestep, updated using the actions you wanted your robot to accomplish. Inference is then refining this prior using the observation that the robot makes.
There's nothing hand-wavy about that; if you do bayesian statistics with bad priors of course you're going to get bad inference. I guess the author just warns about being careful about your assumption which is always good.
- skybrian 7y agoI'm curious what happens when you reboot your robot. What's the first prior?
- bonoboTP 7y agoYou initialize it to something. Like if you have a robot for running around in a specific building or warehouse, you can initialize the location distribution to be uniform within the warehouse. After a few high-quality observations, the effect of the exact choice of initialization should be greatly reduced anyway.
- jtolmar 7y agoFor car GPS, which uses the same math, I used the first GPS signal the device receives. This ends up being equivalent to the uniform prior.
- dTal 7y agoWhat do you do when you wake up? Assume you're in the same place as when you went to sleep. You won't be surprised to find yourself on the other side of the bed - slightly more surprised to find yourself on the floor, and very surprised to find yourself in another country. A large belief update is always a bit of a shock.