Information geometry of statistical inference - an overview

Author

Amari, Shun-Ichi

Author_Institution

Brain Sci. Inst., RIKEN, Wako, Japan

fYear

2002

fDate

20-25 Oct. 2002

Firstpage

86

Lastpage

89

Abstract

The present paper gives a short introduction to information geometry, by using a simple model of an exponential family which is a dually flat Riemannian space. The paper then overviews some of the applications of information geometry: 1) the higher-order asymptotic theory of estimation; 2) semiparametric estimation of the parameter of interest; 3) learning neural networks under the Riemannian structure; and 4) analysis of turbo codes, low density parity check codes and belief propagation algorithm.

Keywords

belief maintenance; inference mechanisms; information theory; learning (artificial intelligence); neural nets; parameter estimation; parity check codes; statistical analysis; turbo codes; LDPC codes; Riemannian structure; belief propagation algorithm; dually flat Riemannian space; higher-order asymptotic theory; information geometry; learning neural networks; low density parity check codes; semiparametric estimation; statistical inference; turbo codes; Algorithm design and analysis; Belief propagation; Estimation theory; Information analysis; Information geometry; Neural networks; Parameter estimation; Parity check codes; Solid modeling; Turbo codes;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory Workshop, 2002. Proceedings of the 2002 IEEE

Print_ISBN

0-7803-7629-3

Type

conf

DOI

10.1109/ITW.2002.1115423

Filename

1115423