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‘Unobserved’ Monte Carlo Methods for Adaptive Algorithms

  • Victor Solo
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 46)

Abstract

Many Signal Processing and Control problems are complicated by the presence of unobserved variables. Even in linear settings this can cause problems in constructing adaptive parameter estimators. In previous work the author investigated the possibility of developing an on-line version of so-called Markov Chain Monte Carlo methods for solving these kinds of problems. In this article we present a new and simpler approach to the same group of problems based on direct simulation of unobserved variables.

Keywords

Markov Chain Monte Carlo Adaptive Algorithm 

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Copyright information

© Springer Science + Business Media, Inc. 2002

Authors and Affiliations

  • Victor Solo
    • 1
  1. 1.School of Electrical Engineering and TelecommunicationsUniversity of New South WalesSydneyAustralia

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