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Modeling Uncertainty pp 373–382Cite as

‘Unobserved’ Monte Carlo Methods for Adaptive Algorithms

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Part of the book series: International Series in Operations Research & Management Science ((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.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, 2052, Australia

    Victor Solo

Authors
  1. Victor Solo
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Editor information

Editors and Affiliations

  1. University of Arizona, USA

    Moshe Dror  & Ferenc Szidarovszky  & 

  2. Université de Montréal, France

    Pierre L’Ecuyer

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© 2002 Springer Science + Business Media, Inc.

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Solo, V. (2002). ‘Unobserved’ Monte Carlo Methods for Adaptive Algorithms. In: Dror, M., L’Ecuyer, P., Szidarovszky, F. (eds) Modeling Uncertainty. International Series in Operations Research & Management Science, vol 46. Springer, New York, NY. https://doi.org/10.1007/0-306-48102-2_18

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