State Space Models with Dynamical and Sparse Variances, and Inference by EM Message Passing

Sparse Bayesian learning (SBL) is a probabilistic approach to estimation problems based on representing sparsity-promoting priors by Normals with Unknown Variances. This representation blends well with linear Gaussian state space models (SSMs). However, in classical SBL the unknown variances are a p...

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Bibliographic Details

Published in:	2019 27th European Signal Processing Conference (EUSIPCO) pp. 1 - 5
Main Authors:	Wadehn, Federico, Weber, Thilo, Loeliger, Hans-Andrea
Format:	Conference Proceeding
Language:	English
Published:	EURASIP 01.09.2019
Subjects:	Biological system modeling Estimation Expectation maximization factor graphs hierarchical state space models Inference algorithms Kalman filters Message passing Signal processing algorithms sparse Bayesian learning
ISSN:	2076-1465
Online Access:	Get full text
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