Online EM Algorithm for Hidden Markov Models
TLDR
In this article, the authors proposed an online parameter estimation algorithm that combines two key ideas: reparameterizing the problem using complete-data sufficient statistics and exploiting a purely recursive form of smoothing in HMMs based on an auxiliary recursion.Citations
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Journal ArticleDOI
On Particle Methods for Parameter Estimation in State-Space Models
TL;DR: A comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models is presented in this article, where the advantages and limitations of these methods are discussed.
Journal ArticleDOI
Online learning with hidden markov models
Gianluigi Mongillo,Sophie Denève +1 more
TL;DR: An online version of the expectation-maximization (EM) algorithm for hidden Markov models (HMMs) is presented, generalized to the case where the model parameters can change with time by introducing a discount factor into the recurrence relations.
Journal ArticleDOI
A survey of techniques for incremental learning of HMM parameters
TL;DR: This paper underscores the need for empirical benchmarking studies among techniques presented in literature, and proposes several evaluation criteria based on non-parametric statistical testing to facilitate the selection of techniques given a particular application domain.
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Forward Smoothing Using Sequential Monte Carlo
TL;DR: This work proposes a new SMC algorithm to compute the expectation of additive functionals recursively and shows how this allows to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.
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Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks
TL;DR: It is shown that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking are implemented.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Book ChapterDOI
A view of the EM algorithm that justifies incremental, sparse, and other variants
TL;DR: In this paper, an incremental variant of the EM algorithm is proposed, in which the distribution for only one of the unobserved variables is recalculated in each E step, which is shown empirically to give faster convergence in a mixture estimation problem.