Fisher information metric and Poisson kernels
スポンサーリンク
概要
- 論文の詳細を見る
A complete Riemannian manifold X with negative curvature satisfying －b2less-than-or-equals, slantKXless-than-or-equals, slant－a2<0 for some constants a,b, is naturally mapped in the space of probability measures on the ideal boundary ∂X by assigning the Poisson kernels. We show that this map is embedding and the pull-back metric of the Fisher information metric by this embedding coincides with the original metric of X up to constant provided X is a rank one symmetric space of non-compact type. Furthermore, we give a geometric meaning of the embedding.
- Elsevier B.V.の論文
Elsevier B.V. | 論文
- Analysis of magnetic anisotropy for Co2Cr0.6Fe0.4Al thin films epitaxially grown on GaAs
- Lattice dynamics calculation of infrared active modes of cuprate superconductors
- Developmental biotechnology for aquaculture, with special reference to surrogate production in teleost fishes
- Two-component bacterial multidrug transporter, EbrAB: Mutations making each component solely functional
- On the ionization energy of CF3SF5 in the valence region measured by angle-resolved photoelectron spectroscopy