4 ms·
Beyond what the other replies are saying, the general pattern is also highly applicable. You can keep O(1) memory, update it on every new piece of unreliable da
by mabbo 10y ago
Beyond what the other replies are saying, the general pattern is also highly applicable. You can keep O(1) memory, update it on every new piece of unreliable data, and continually have a better understanding of what the truth is.
Consider standard deviation. You can calculate the standard deviation of a stream of numbers without storing all of them, or knowing where the stream will end. 'The standard deviation so far', in effect.
- amelius 10y ago> Consider standard deviation. You can calculate the standard deviation of a stream of numbers without storing all of them, or knowing where the stream will end. 'The standard deviation so far', in effect. Yes, you are right, but this is both a good example, and a bad example, because my old Casio calculator could do that too :)
- kurthr 10y agoExactly, the UKF and EKF filters are very highly optimized for memory size and computation while dealing with noisy measurements non-linear and difficult to compute functions. They certainly don't require normally distributed errors. That makes them applicable to a wide range of embedded applications. As generalized ML tools, I'm pretty doubtful unless you wanted to create something in hardware as a large set of coupled noisy state-spaces. http://ethw.org/First-Hand:The_Unscented_Transform http://ethw.org/First-Hand:The_Unscented_Transform