Conventional hidden Markov models provide a discrete distribution over a finite number of states. In some modeling scenarios, particularly those representing data from a physical systems, such discrete states are, at best, an idealization, since the physical system may exhibit a continuous transition between states. In this paper, a hidden Markov model is presented that generalizes this by introduction of a state that may take any value in a simplex. The Dirichlet distribution is used to provide a representation of the probability distribution of the states. The transition probability density is assumed to be Dirichlet, and the output distribution is assumed to be a state-dependent mixture. An estimation of the state distribution using propagation and update steps is developed. Approximations of the state estimates remain in the set of Dirichlet distributions, so computationally efficient state propagation is possible. A forward/backward (smoothing) algorithm is also developed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Continuum-State Hidden Markov Models with Dirichlet State Distributions


    Beteiligte:
    Moon, Todd K. (Autor:in) / Gunther, Jacob H. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2015-01-08


    Format / Umfang :

    25 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Traffic lights detection and state estimation using Hidden Markov Models

    Gomez, Andres E. / Alencar, Francisco A. R. / Prado, Paulo V. et al. | IEEE | 2014


    TRAFFIC LIGHTS DETECTION AND STATE ESTIMATION USING HIDDEN MARKOV MODELS

    Gomez, A. / Alencar, F. / Prado, P. et al. | British Library Conference Proceedings | 2014


    Contact State Estimation using Multiple Model Estimation and Hidden Markov Models

    Debus, Thomas / Dupont, Pierre / Howe, Robert | Springer Verlag | 2003


    Continuous Driver Intention Recognition with Hidden Markov Models

    Berndt, Holger / Emmert, Jorg / Dietmayer, Klaus | IEEE | 2008


    Crash Detection System Using Hidden Markov Models

    Singh, G. B. / Song, H. / Chou, C. C. et al. | British Library Conference Proceedings | 2004