You would have to encode the distributions as vectors. For discrete distributions, I think that this is relatively trivial since you could interpret each vector entry as the probability for an element i of the domain of the distribution. I think that would result in the Hellinger distance [1] being defined as:
HD(P, Q) = \sum_i (\sqrt(p_i) - \sqrt(q_i) ) This makes it look a lot like L_0.5 which we already have. Perhaps the original poster can clarify if this is what they want? [1] http://en.wikipedia.org/wiki/Hellinger_distance On Wed, Jul 13, 2011 at 2:14 PM, Sean Owen <[email protected]> wrote: > How do you apply this metric to vectors? >
