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A. P. Dempster, N. M. Laird, and D. B. Rubin.
Maximum likelihood from incomplete data via the EM algorithm.
J. Royal Statist. Soc. Ser. B, 39(1):1-38 (with discussion),
1977.
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C. F. J. Wu.
On the convergence properties of the EM algorithm.
Annals of Statistics, 11(1):T95-103, 1983.
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L. R. Rabiner and B-H. Juang.
Fundamentals of speech recognition.
Prentice-Hall, 1993.
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L. R. Rabiner.
A tutorial on hidden Markov models and selected applications in
speech recognition.
Proc. IEEE, 77(2):257-285, February 1989.
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C. Fraley and A. E. Raftery.
How many clusters? Which clustering method? Answers via
model-based cluster analysis.
Technical Report 329, University of Washington, Department of
statistics, 1998.
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J-L. Gauvain and C-H. Lee.
Maximum a posteriori estimation for multivariate gaussian mixture
observations of Markov chains.
IEEE Trans. Speech and Audio Processing, 2(2):291-298, April
1994.
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O. Cappé, C. Mokbel, D. Jouvet, and E. Moulines.
An algorithm for maximum likelihood estimation of hidden Markov
models with unknown state-tying.
IEEE Trans. Speech and Audio Processing, 6(1):61-70, January
1998.
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N. L. Johnson and S. Kotz.
Discrete Distributions, volume 2.
Wiley, 1969.
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J. Grandell.
Mixed Poisson Processes.
Chapman & Hall, 1997.
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C. K. Carter and R. Kohn.
On Gibbs sampling for state space models.
Biometrika, 81(3):541-553, 1994.
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X-L. Meng and D. B. Rubin.
Maximum likelihood estimation via the ECM algorithm: A general
framework.
Biometrika, 80(2):267-278, 1993.
Olivier Cappé, Aug 24 2001