# Differential Geometry for Statistical and Entropy-Based Inference

**Jun Zhang**(Department of Psychology and Department of Mathematics, University of Michigan-Ann Arbor)

November 03, 2017 — 10:30 — Location: Salle du conseil du L2S

###### Abstract

Information Geometry is the differential geometric study of the
manifold of probability models, and promises to be a unifying
geometric framework for investigating statistical inference,
information theory, machine learning, etc. Instead of using metric for
measuring distances on such manifolds, these applications often use
“divergence functions” for measuring proximity of two points (that do
not impose symmetry and triangular inequality), for instance Kullback-
Leibler divergence, Bregman divergence, f-divergence, etc. Divergence
functions are tied to generalized entropy (for instance, Tsallis
entropy, Renyi entropy, phi-entropy, U-entropy) and corresponding
cross-entropy functions. It turns out that divergence functions enjoy
pleasant geometric properties – they induce what is called
“statistical structure” on a manifold M: a Riemannian metric g
together with a pair of torsion-free affine connections D, D*, such
that D and D* are both Codazzi coupled to g while being conjugate to
each other. We use these concepts to investigate a generalization of
Maximum Entropy principle through conjugate rho-tau embedding
mechanism. We show how this generalization captures the various
generalization of MaxEnt, including deform-logarithm model and
U-model. (Work in collaboration with Jan Naudts)