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- W2103198577 abstract "A sequential multiple hypotheses test for the unknown parameters of a Gaussian distribution is developed. The case of a known variance but an unknown mean belonging to the set (00,01,''' ,Om_l} is considered as well as that of a known mean but an unknown variance belonging to the set {sigma^{2}_{0}, sigma^{2}_{1},cdots ,sigma^{2}_{m-1}} . Analytical expressions are developed for the algorithm performance, i.e., the average length of the test and the error probability. The results are compared with a Bayes algorithm and show that the new algorithm needs about half as many samples. As far as the authors are aware this is the first m ary sequential algorithm for which the performance has been found analytically. Because of the performance of this algorithm and the fact that it is a natural generalization of the binary sequential test, it is felt that the algorithm may be optimum or close to optimum." @default.
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- W2103198577 date "1980-03-01" @default.
- W2103198577 modified "2023-09-27" @default.
- W2103198577 title "A sequential multiple hypotheses test for the unknown parameters of a Gaussian distribution (Corresp.)" @default.
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- W2103198577 doi "https://doi.org/10.1109/tit.1980.1056152" @default.
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