Bayesian analysis of finite Gaussian mixtures

Mark R. Morelande, Branko Ristic
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
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 this
more » ... m keeps only a prede ned number of the highest ranked label sequences with the ranking based on posterior probabilities. We show by numerical simulations that the proposed technique consistently outperforms both the k-means and the EM algorithm.
doi:10.1109/icassp.2010.5495791 dblp:conf/icassp/MorelandeR10 fatcat:midentb3wnecfjqngiox7dqgna