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Information geometry of statistical inference - an overview | IEEE Conference Publication | IEEE Xplore

Information geometry of statistical inference - an overview


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 ...Show More

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.
Date of Conference: 25-25 October 2002
Date Added to IEEE Xplore: 06 January 2003
Print ISBN:0-7803-7629-3
Conference Location: Bangalore, India

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