Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

0370444 - ÚTIA 2012 RIV GB eng J - Journal Article
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. E-ISSN 1532-4141
R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567
Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16
Institutional research plan: CEZ:AV0Z10750506
Keywords : Bayesian methods * Particle filters * Recursive estimation
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.295, year: 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.
Permanent Link: http://hdl.handle.net/11104/0204246