Empirical Bayesian learning in AR graphical models
Copyright policy )Empirical Bayesian learning in AR graphical models
We address the problem of learning graphical models which correspond to high dimensional autoregressive stationary stochastic processes. A graphical model describes the conditional dependence relations among the components of a stochastic process and represents an important tool in many fields. We propose an empirical Bayes estimator of sparse autoregressive graphical models and latent-variable autoregressive graphical models. Numerical experiments show the benefit to take this Bayesian perspective for learning these types of graphical models.
Automatica (accepted)
- University of Padua Italy
FOS: Computer and information sciences, convex optimization, convex relaxation, sparsity, Learning and adaptive systems in artificial intelligence, Convex optimization; Convex relaxation; Empirical Bayesian learning; Sparsity and low rank inducing priors, Methodology (stat.ME), Time series, auto-correlation, regression, etc. in statistics (GARCH), Optimization and Control (math.OC), FOS: Mathematics, Inference from stochastic processes and spectral analysis, empirical Bayesian learning, low-rank inducing priors, Mathematics - Optimization and Control, Statistics - Methodology, Probabilistic graphical models
FOS: Computer and information sciences, convex optimization, convex relaxation, sparsity, Learning and adaptive systems in artificial intelligence, Convex optimization; Convex relaxation; Empirical Bayesian learning; Sparsity and low rank inducing priors, Methodology (stat.ME), Time series, auto-correlation, regression, etc. in statistics (GARCH), Optimization and Control (math.OC), FOS: Mathematics, Inference from stochastic processes and spectral analysis, empirical Bayesian learning, low-rank inducing priors, Mathematics - Optimization and Control, Statistics - Methodology, Probabilistic graphical models
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