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Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

SYSNO ASEP	0370444
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Článek ve WOS
Title	Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
Author(s)	Dedecius, Kamil (UTIA-B)_{RID, ORCID} Hofman, Radek (UTIA-B)_RID
Number of authors	2
Source Title	Communications in Statistics - Simulation and Computation. - : Taylor & Francis - ISSN 0361-0918 Roč. 41, č. 5 (2012), s. 582-589
Number of pages	8 s.
Language	eng - English
Country	GB - United Kingdom
Keywords	Bayesian methods ; Particle filters ; Recursive estimation
Subject RIV	BB - Applied Statistics, Operational Research
R&D Projects	VG20102013018 GA MV - Ministry of Interior (MV)
	GA102/08/0567 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z10750506 - UTIA-B (2005-2011)
UT WOS	000301342800002
DOI	10.1080/03610918.2011.598992
Annotation	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.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
Year of Publishing	2012

Number of the records: 1