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2013 Online Expectation Maximization based algorithms for inference in Hidden Markov Models
Sylvain Le Corff, Gersende Fort
Electron. J. Statist. 7: 763-792 (2013). DOI: 10.1214/13-EJS789

Abstract

The Expectation Maximization (EM) algorithm is a versatile tool for model parameter estimation in latent data models. When processing large data sets or data stream however, EM becomes intractable since it requires the whole data set to be available at each iteration of the algorithm. In this contribution, a new generic online EM algorithm for model parameter inference in general Hidden Markov Model is proposed. This new algorithm updates the parameter estimate after a block of observations is processed (online). The convergence of this new algorithm is established, and the rate of convergence is studied showing the impact of the block-size sequence. An averaging procedure is also proposed to improve the rate of convergence. Finally, practical illustrations are presented to highlight the performance of these algorithms in comparison to other online maximum likelihood procedures.

Citation

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Sylvain Le Corff. Gersende Fort. "Online Expectation Maximization based algorithms for inference in Hidden Markov Models." Electron. J. Statist. 7 763 - 792, 2013. https://doi.org/10.1214/13-EJS789

Information

Received: 1 October 2012; Published: 2013
First available in Project Euclid: 25 March 2013

zbMATH: 1336.62090
MathSciNet: MR3040559
Digital Object Identifier: 10.1214/13-EJS789

Subjects:
Primary: 60J22, 62F12, 62L12
Secondary: 62L20, 65C60

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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