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‘Unobserved’ Monte Carlo Methods for Adaptive Algorithms

In: Modeling Uncertainty

Author

Listed:
  • Victor Solo

    (University of New South Wales)

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.

Suggested Citation

  • Victor Solo, 2002. "‘Unobserved’ Monte Carlo Methods for Adaptive Algorithms," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 373-382, Springer.
    • Handle: RePEc:spr:isochp:978-0-306-48102-4_18
    DOI: 10.1007/0-306-48102-2_18
    as

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