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Adaptive algorithms and Markov chain Monte Carlo methods

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1 Author(s)
Solo, V. ; Dept. of Stat., Macquarie Univ., North Ryde, NSW, Australia

Many signal processing and control problems are complicated by the presence of unobserved variables and/or auxiliary variables measured with error. In nonlinear settings this causes problems in constructing adaptive parameter estimators. In off-line situations so-called Markov chain Monte Carlo methods have recently become popular for solving these kinds of problems. In this paper we explore the development of online Markov chain Monte Carlo techniques for adaptive parameter estimation

Published in:

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on  (Volume:2 )

Date of Conference:

1999

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