Graphical Inference in Linear-Gaussian State-Space Models

State-space models (SSM) are central to describe time-varying complex systems in countless signal processing applications such as remote sensing, networks, biomedicine, and finance to name a few. Inference and prediction in SSMs are possible when the model parameters are known, which is rarely the c...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 70; S. 4757 - 4771
Hauptverfasser:	Elvira, Victor, Chouzenoux, Emilie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Complex systems Convexity EM algorithm Engineering Sciences Estimation Graph theory graphical inference Inference Kalman filtering Kalman filters Mathematical models Numerical models Optimization Parameters primal-dual algorithms proximal methods Remote sensing Signal and Image processing Signal processing Signal processing algorithms Sparse matrices sparsity State space models State-space methods State-space modeling Statistical analysis Time series analysis graphical inference proximal methods primal-dual algorithms sparsity Kalman filtering EM algorithm State-space modeling
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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