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A decision-directed detection scheme for multiple hypotheses is developed and analyzed. It is assumed that the probability density functions\{fᵢ(x)\}under each of them+1hypotheses are known, and the prior probablities\{πᵢ\}are unknown and sequentially estimated on the basis of previous decisions. Using a set of nonlinear transformations of the data and applying results from the stochastic approximation theory, improved algorithms are given for achieving asymptotically unbiased estimates and accelerated convergence to the true priors.
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Kazakos et al. (1980) studied this question.
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