Identification of Non-causal Graphical Models

Autor:	You, Junyao, Zorzi, Mattia
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to the IEEE CDC 2024 conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.09480 Zobrazit plný text záznamu View this record from Arxiv