Assume V_(n-1) = median(X) for the data stream X_n. Now we have
E[V_n | V_(n-1)] = V_(n-1) + E[signum(V_(n-1) - X_n)]
but by the definition of the median, the right hand side expectation is 0, so the random process is fixed in expectation.Proving it'll converge to that value would be harder, though. It'll probably depend on the data being much larger than the step size (which is 1).
"In a normal distribution the probability of a value higher or lower than mean is 50%. So if you find mean, the average of your +1/-1 noise is zero -> if you're on mean you'll remain there.
If you're not on mean you have a (50 + error)% chance of moving towards mean and (50 - error)% chance of moving away from mean on the next random value -> the trend is towards mean."
... I'm not following. I can see my reply a few posts down. I accidentally hit submit twice, deleted one but the second one is still there.
Edit: The same technique can be found in some 1-bit ADCs, I believe it's delta-sigma ADC.
Reminds me of a similar comment in the margin of a old book: http://en.wikipedia.org/wiki/Fermats_Last_Theorem. :)
But it does sound both plausible and brilliant. How do you prove it (in broad terms)?