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Sequential estimation of random parameters under model uncertainty

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1 Author(s)
Djuric, P.M. ; Dept. of Electr. & Comput. Eng., State Univ. of New York, Stony Brook, NY

In many signal processing problems, the estimation of random parameters must be carried out sequentially and under model uncertainty. In the paper, a Bayesian approach is proposed for solving this problem, which is based on sequential updating of the posterior distribution of the desired parameters. It is shown that under a certain general set of conditions, the posterior of the unknown parameters is a mixture density. Since the computation of the solution becomes very intensive as the number of data (records) grows, a numerical procedure is proposed based on the sequential importance sampling scheme. Its number of computations per new data record is constant, and the procedure can easily be implemented in parallel

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Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:1 )

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