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Journal Article

Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

Dedecius Kamil, Hofman Radek

: Communications in Statistics - Simulation and Computation vol.41, 5 (2012), p. 582-589

: CEZ:AV0Z10750506

: VG20102013018, GA MV, GA102/08/0567, GA ČR, SGS 10/099/OHK3/1T/16, ČVUT

: Bayesian methods, Particle filters, Recursive estimation

: 10.1080/03610918.2011.598992

: http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf

(eng): 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.

: BB