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- W2957601030 abstract "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." @default.
- W2957601030 created "2019-07-23" @default.
- W2957601030 creator A5088792059 @default.
- W2957601030 date "2019-07-08" @default.
- W2957601030 modified "2023-09-27" @default.
- W2957601030 title "Empirical Bayesian Learning in AR Graphical Models" @default.
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