Empirical Bayesian learning in AR graphical models

DOI10.1016/j.automatica.2019.108516zbMath1496.62160arXiv1907.03829OpenAlexW2964468196WikidataQ127393825 ScholiaQ127393825MaRDI QIDQ2280924

Mattia Zorzi

Publication date: 19 December 2019

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.03829

zbMATH Keywords

convex optimizationconvex relaxationsparsityempirical Bayesian learninglow-rank inducing priors

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items

New reproducing kernel functions in the reproducing kernel Sobolev spaces, Generalized maximum entropy based identification of graphical ARMA models, Adaptive regularised kernel-based identification method for large-scale systems with unknown order, Support Recovery and Parameter Identification of Multivariate ARMA Systems with Exogenous Inputs, Autoregressive identification of Kronecker graphical models, Graphical model selection for a particular class of continuous-time processes

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