Identification of Non-causal Graphical Models

• URL: http://arxiv.org/abs/2410.09480v1
• Date: Sat, 12 Oct 2024 10:40:46 GMT
• Title: Identification of Non-causal Graphical Models
• Authors: Junyao You, Mattia Zorzi,
• Abstract summary: The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We propose a new covariance extension problem and show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. Then, we generalize the paradigm to a class of graphical autoregressive moving-average models. Finally, we test the performance of the proposed method through some numerical experiments.

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