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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN"><!--Converted with LaTeX2HTML 2K.1beta (1.48)original version by: Nikos Drakos, CBLU, University of Leeds* revised and updated by: Marcus Hennecke, Ross Moore, Herb Swan* with significant contributions from: Jens Lippmann, Marek Rouchal, Martin Wilck and others --><HTML><HEAD><TITLE>Bibliography</TITLE><META NAME="description" CONTENT="Bibliography"><META NAME="keywords" CONTENT="H2M, H2M/cnt, Hidden Markov Model, HMM, Mixture model, Vector Quantization, Expectation Maximization, EM, Multivariate Gaussian, Count data, Poisson, Negative binomial, MATLAB, OCTAVE, GPL"><META NAME="resource-type" CONTENT="document"><META NAME="distribution" CONTENT="global"><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso-8859-1"><META NAME="Generator" CONTENT="LaTeX2HTML v2K.1beta"><META HTTP-EQUIV="Content-Style-Type" CONTENT="text/css"><LINK REL="STYLESHEET" HREF="h2m.css"><LINK REL="previous" HREF="node22.html"><LINK REL="up" HREF="h2m.html"></HEAD><BODY BGCOLOR="ivory"><!--Navigation Panel--><B>Up:</B> <A NAME="tex2html378" HREF="h2m.html">H2M : A set</A><B> Previous:</B> <A NAME="tex2html374" HREF="node22.html">Downloading H2M</A><P><!--End of Navigation Panel--><H2><A NAME="SECTION00080000000000000000">Bibliography</A></H2><DL COMPACT><DD><P></P><DT><A NAME="Dempster:EM">1</A><DD>A. P. Dempster, N. M. Laird, and D. B. Rubin.<BR>Maximum likelihood from incomplete data via the EM algorithm.<BR><EM>J. Royal Statist. Soc. Ser. B</EM>, 39(1):1-38 (with discussion), 1977.<P></P><DT><A NAME="Wu:EM">2</A><DD>C. F. J. Wu.<BR>On the convergence properties of the EM algorithm.<BR><EM>Annals of Statistics</EM>, 11(1):T95-103, 1983.<P></P><DT><A NAME="Rabiner:SpeechRec">3</A><DD>L. R. Rabiner and B-H. Juang.<BR><EM>Fundamentals of speech recognition</EM>.<BR>Prentice-Hall, 1993.<P></P><DT><A NAME="Rabiner:HMM">4</A><DD>L. R. Rabiner.<BR>A tutorial on hidden Markov models and selected applications in speech recognition.<BR><EM>Proc. IEEE</EM>, 77(2):257-285, February 1989.<P></P><DT><A NAME="FR98hmc">5</A><DD>C. Fraley and A. E. Raftery.<BR>How many clusters? Which clustering method? Answers via model-based cluster analysis.<BR>Technical Report 329, University of Washington, Department of statistics, 1998.<P></P><DT><A NAME="Gauvain:MAPHMM">6</A><DD>J-L. Gauvain and C-H. Lee.<BR>Maximum a posteriori estimation for multivariate gaussian mixture observations of Markov chains.<BR><EM>IEEE Trans. Speech and Audio Processing</EM>, 2(2):291-298, April 1994.<P></P><DT><A NAME="Cappe:ShDist">7</A><DD>O. Cappé, C. Mokbel, D. Jouvet, and E. Moulines.<BR>An algorithm for maximum likelihood estimation of hidden Markov models with unknown state-tying.<BR><EM>IEEE Trans. Speech and Audio Processing</EM>, 6(1):61-70, January 1998.<P></P><DT><A NAME="JK69dd">8</A><DD>N. L. Johnson and S. Kotz.<BR><EM>Discrete Distributions</EM>, volume 2.<BR>Wiley, 1969.<P></P><DT><A NAME="Grandell:MixPoisson">9</A><DD>J. Grandell.<BR><EM>Mixed Poisson Processes</EM>.<BR>Chapman & Hall, 1997.<P></P><DT><A NAME="Carter:GibbsState">10</A><DD>C. K. Carter and R. Kohn.<BR>On Gibbs sampling for state space models.<BR><EM>Biometrika</EM>, 81(3):541-553, 1994.<P></P><DT><A NAME="Meng:ECM">11</A><DD>X-L. Meng and D. B. Rubin.<BR>Maximum likelihood estimation via the ECM algorithm: A general framework.<BR><EM>Biometrika</EM>, 80(2):267-278, 1993.</DL><P><BR><HR><ADDRESS>Olivier Cappé, Aug 24 2001</ADDRESS></BODY></HTML>
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