Počet záznamů: 1  

Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

  1. 1.
    0370444 - UTIA-B 2012 RIV GB eng J - Článek v odborném periodiku
    Dedecius, Kamil - Hofman, Radek
    Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter.
    Communications in Statistics: Simulation and Computation. Roč. 41, č. 5 (2012), s. 582-589. ISSN 0361-0918
    Grant CEP: GA MV VG20102013018; GA ČR GA102/08/0567
    Grant ostatní:ČVUT(CZ) SGS 10/099/OHK3/1T/16
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: Bayesian methods * Particle filters * Recursive estimation
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    Impakt faktor: 0.295, rok: 2012
    http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf

    The authors are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. They propose a linear regression model within Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixture, where the weights of components are tuned with a particle filter. The mixture reflects a priori given hypotheses on different scenarios of (expected) parameters' evolution.
    Trvalý link: http://hdl.handle.net/11104/0204246