3 research outputs found
A categorical foundation for Bayesian probability
Given two measurable spaces and with countably generated
-algebras, a perfect prior probability measure on and a
sampling distribution , there is a corresponding inference
map which is unique up to a set of measure zero. Thus,
given a data measurement , a posterior probability
can be computed. This procedure is iterative: with
each updated probability , we obtain a new joint distribution which in
turn yields a new inference map and the process repeats with each
additional measurement. The main result uses an existence theorem for regular
conditional probabilities by Faden, which holds in more generality than the
setting of Polish spaces. This less stringent setting then allows for
non-trivial decision rules (Eilenberg--Moore algebras) on finite (as well as
non finite) spaces, and also provides for a common framework for decision
theory and Bayesian probability.Comment: 15 pages; revised setting to more clearly explain how to incorporate
perfect measures and the Giry monad; to appear in Applied Categorical
Structure
Gait Recognition in the Presence of Occlusion: A New Dataset and Baseline Algorithms
Human gait is an important biometric feature for identification of people. In this paper we present a new dataset for gait
recognition. The presented database overcomes a crucial limitation of other state-of-the-art gait recognition databases. More
specifically this database addresses the problem of dynamic and static inter object occlusion. Furthermore this dataset offers
three new kinds of gait variations, which allow for challenging evaluation of recognition algorithms. In addition to presenting
the database we present two baseline algorithms (Color histograms, Gait Energy Image) to perform person identification using
gait. These algorithms already show promising results on the presented database