Online EM Algorithm for Hidden Markov Models

@inproceedings{Capp2009OnlineEA,
  title={Online EM Algorithm for Hidden Markov Models},
  author={Olivier Capp{\'e}},
  year={2009}
}
  • Olivier Cappé
  • Published 2009
  • Mathematics
  • Online (also called “recursive” or “adaptive”) estimation of fixed model parameters in hidden Markov models is a topic of much interest in times series modeling. In this work, we propose an online parameter estimation algorithm that combines two key ideas. The first one, which is deeply rooted in the Expectation-Maximization (EM) methodology, consists in reparameterizing the problem using complete-data sufficient statistics. The second ingredient consists in exploiting a purely recursive form… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Figures and Tables from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 99 CITATIONS

    Maximum likelihood parameter estimation in time series models using sequential Monte Carlo

    VIEW 23 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Advancements in latent space network modelling

    VIEW 6 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    A Semi-Supervised and Online Learning Approach for Non-Intrusive Load Monitoring

    VIEW 6 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Mini-batch learning of exponential family finite mixture models

    VIEW 8 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    PROPS: Probabilistic personalization of black-box sequence models

    VIEW 6 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Online estimation of driving events and fatigue damage on vehicles

    VIEW 8 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    FILTER CITATIONS BY YEAR

    2009
    2020

    CITATION STATISTICS

    • 31 Highly Influenced Citations

    • Averaged 10 Citations per year from 2017 through 2019

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 26 REFERENCES

    A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Online Learning with Hidden Markov Models

    VIEW 18 EXCERPTS
    HIGHLY INFLUENTIAL

    Inference in hidden Markov models

    VIEW 4 EXCERPTS

    State Space and Hidden Markov Models

    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    Characterization of single channel currents using digital signal processing techniques based on Hidden Markov Models.

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    An EM Algorithm for Ion-Channel Current Estimation

    VIEW 2 EXCERPTS