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The problem considered in this paper is parameter estimation of a multivariate Gaussian mixture distribution with a known number of components. The paper presents a new Bayesian method which sequentially processes the observed data points by forming candidate sequences of labels assigning data points to mixture components. Using conjugate priors, we derive analytically a recursive formula for the computation of the probability of each label sequence. The practical implementation of thisdoi:10.1109/icassp.2010.5495791 dblp:conf/icassp/MorelandeR10 fatcat:midentb3wnecfjqngiox7dqgna