Ecole d'ingénieur et centre de recherche en Sciences du numérique

Gaussian variational Bayes Kalman filtering for dynamic sparse Bayesian learning

Kurisummoottil Thomas, Christo; Slock, Dirk TM

ITISE 2018, 5th International Conference on Time Series and Forecasting, 19-21 September 2018, Granada, Spain

Sparse Baysesian Learning (SBL) provides sophisticated (state) model order selection with unknown support distribution. This allows to handle problems with big state dimensions and relatively limited data. The techniques proposed in this paper allow to handle the extension of SBL to time-varying states, modeled as diagonal rst-order vector auto-regressive (VAR(1)) processes with unknown parameters. Adding the parameters to the state leads to an augmented state and a non-linear (at least bilinear) state-space model. The proposed approach, which applies also to more general non-linear models, uses Variational Bayes (VB) techniques to approximate the posterior distribution by a factored form, with Gaussian or exponential factors. The granularity of the factorization can take on various levels. In one extreme instance, called Gausian Space Alternating Variational Estimation Kalman Filtering (GSAVE-KF), all state components are treated individually, leading to low complexity ltering. Simulations illustrate the performance of the proposed GVB-KF techniques, which represent an alternative to Linear MMSE (LMMSE) ltering.

Document Bibtex

Titre:Gaussian variational Bayes Kalman filtering for dynamic sparse Bayesian learning
Mots Clés:Sparse Bayesian Learning, Variational Bayes, Kalman Filtering
Type:Conférence
Langue:English
Ville:Granada
Pays:ESPAGNE
Date:
Département:Systèmes de Communication
Eurecom ref:5623
Copyright: © Springer. Personal use of this material is permitted. The definitive version of this paper was published in ITISE 2018, 5th International Conference on Time Series and Forecasting, 19-21 September 2018, Granada, Spain and is available at :
Bibtex: @inproceedings{EURECOM+5623, year = {2018}, title = {{G}aussian variational {B}ayes {K}alman filtering for dynamic sparse {B}ayesian learning}, author = {{K}urisummoottil {T}homas, {C}hristo and {S}lock, {D}irk {TM}}, booktitle = {{ITISE} 2018, 5th {I}nternational {C}onference on {T}ime {S}eries and {F}orecasting, 19-21 {S}eptember 2018, {G}ranada, {S}pain}, address = {{G}ranada, {ESPAGNE}}, month = {09}, url = {http://www.eurecom.fr/publication/5623} }
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