Information geometry / Nihat Ay, Jürgen Jost, Hông Vân Lê, Lorenz Schwachhöfer.

Author
Ay, Nihat, 1970- [Browse]
Format
Book
Language
English
Published/​Created
  • Cham : Springer, [2017]
  • ©2017
Description
xi, 407 pages : illustrations ; 25 cm.

Details

Subject(s)
Geometrical models in statistics [Browse]
Author
Series
  • Ergebnisse der Mathematik und ihrer Grenzgebiete ; 3. Folge, Bd. 64. [More in this series]
  • Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge ; volume 64
Summary note
"The book provides a comprehensive introduction and a novel mathematical foundation of the field of information geometry with complete proofs and detailed background material on measure theory, Riemannian geometry and Banach space theory. Parametrised measure models are defined as fundamental geometric objects, which can be both finite or infinite dimensional. Based on these models, canonical tensor fields are introduced and further studied, including the Fisher metric and the Amari-Chentsov tensor, and embeddings of statistical manifolds are investigated.This novel foundation then leads to application highlights, such as generalizations and extensions of the classical uniqueness result of Chentsov or the Cramér-Rao inequality. Additionally, several new application fields of information geometry are highlighted, for instance hierarchical and graphical models, complexity theory, population genetics, or Markov Chain Monte Carlo. The book will be of interest to mathematicians who are interested in geometry, information theory, or the foundations of statistics, to statisticians as well as to scientists interested in the mathematical foundations of complex systems."-- Provided by publisher.
Bibliographic references
Includes bibliographical references and index.
Contents
Introduction -- Finite information geometry -- Parametrized measure models -- The intrinsic geometry of statistical models -- Information geometry and statistics -- Fields of application of information geometry.
Other format(s)
Also available in an electronic version.
ISBN
  • 3319564773
  • 9783319564777
LCCN
2017951855
OCLC
975026689
Other views
Staff view