Computational Information Geometry Wonderland

I attended one talk on "noise is signal". The basic idea is to use the prior that noise should be symmetric so that the observed noise in images that has been deformed by the camera response function should be analyzed to recover the curve. This seems to work fine. Also, Matsushita-san introduced a "metric" (a similarity measure) with some insights of noise. The talk was instructive.

Another talk was about Clifford algebra (and geometric algebra interpretation). Here the basic primitive is not point but sphere and the vectors are splitted into 3 categories for inner products of basis equal to 1, -1, or 0. This seems to be a full generalization of the many anterior algebras. I am curious to hear about differential extensions of it. Tomorrow is the application day. Looking forward to hearing part II!