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Estimation in the Presence of Hidden Variables

  • Shun-ichi Amari
Chapter
Part of the Applied Mathematical Sciences book series (AMS, volume 194)

Abstract

Let us consider a statistical model \(M= \left\{ p({\varvec{x}}, {\varvec{\xi }}) \right\} \), where vector random variable \({\varvec{x}}\) is divided into two parts \({\varvec{x}}= \left( {\varvec{y}}, {\varvec{h}}\right) \) so that \(p({\varvec{x}}, {\varvec{\xi }}) = p ({\varvec{y}}, {\varvec{h}} ; {\varvec{\xi }})\). When \({\varvec{x}}\) is not fully observed but \({\varvec{y}}\) is observed, \({\varvec{h}}\) is called a hidden variable.

Keywords

Gaussian Mixture Model Fisher Information Hide Variable Exponential Family Restricted Boltzmann Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Japan 2016

Authors and Affiliations

  1. 1.Brain Science InstituteRIKENWakoJapan

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