Particle based smoothed marginal MAP estimation for general state space models

S. Saha, Pranab K. Mandal, Arunabha Bagchi, Y. Boers, J.N. Driessen

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    8 Citations (Scopus)
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    We consider the smoothing problem for a general state space system using sequential Monte Carlo (SMC) methods. The marginal smoother is assumed to be available in the form of weighted random particles from the SMC output. New algorithms are developed to extract the smoothed marginal maximum a posteriori (MAP) estimate of the state from the existing marginal particle smoother. Our method does not need any kernel fitting to obtain the posterior density from the particle smoother. The proposed estimator is then successfully applied to find the unknown initial state of a dynamical system and to address the issue of parameter estimation problem in state space models.
    Original languageUndefined
    Pages (from-to)264-273
    Number of pages10
    JournalIEEE transactions on signal processing
    Issue number2
    Publication statusPublished - Jan 2013


    • EWI-23083
    • Maximum a posteriori
    • Unknown initial conditions
    • IR-84237
    • Particle smoother
    • METIS-296447
    • Sequential Monte Carlo

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