Exactly, 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

> 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 :)