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Learning Networks from Non-Invasive Observations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning Networks from Non-Invasive Observations./
作者:	Dimovska, Mihaela.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	152 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28540709
ISBN:	9798534691870

Learning Networks from Non-Invasive Observations.
Dimovska, Mihaela.

Learning Networks from Non-Invasive Observations. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 152 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of Minnesota, 2021.

This item must not be sold to any third party vendors.

Learning the underlying structure of a networked dynamic system from observational data is an important problem in many domains, from climate studies to economics. One of the most well-known approaches to this problem is Granger causality, which relies on the premise that data are sampled at a frequency sufficient to capture the cause-to-effect delays, leading to strictly causal observed dynamics. For such strictly causal systems, it has been shown that Granger causality consistently reconstructs the underlying graph of the network. However, in many domains, such as finance, neuroscience or climate studies, the observed dynamics does not follow the strict causality assumption. Thus, many reconstruction methods that try to deal with non-strictly causal dynamics have been developed in the last decade. These methods, however, tend to put limiting assumptions on the graph structures of networks. Furthermore, as we show in this dissertation, the network reconstruction problem is not well-posed in the non-strictly causal case, as in general, it does not admit a unique solution. Thus, many of the existing methods also do not provide any theoretical guarantees for the learned network. In this work, we develop network reconstruction methods for a large class of networks with non-strictly causal dynamics. We provide theoretical guarantees for the reconstruction, while posing no limitations on the underlying structure. We also address the ill-posedness of the network reconstruction problem. The only required assumption in the novel methods is that at least one strictly causal operator is present in every feedback loop. We also provide orientation rules that can orient some of the non-strictly causal links in the network. We test the methods on several benchmark examples, on random networks via simulations, and we also apply them on real-world datasets that show the effectiveness of the proposed algorithms.

ISBN: 9798534691870Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Dynamic systems

Learning Networks from Non-Invasive Observations.
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300 $a 152 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Materassi, Donatello.
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506 $a This item must not be sold to any third party vendors.
520 $a Learning the underlying structure of a networked dynamic system from observational data is an important problem in many domains, from climate studies to economics. One of the most well-known approaches to this problem is Granger causality, which relies on the premise that data are sampled at a frequency sufficient to capture the cause-to-effect delays, leading to strictly causal observed dynamics. For such strictly causal systems, it has been shown that Granger causality consistently reconstructs the underlying graph of the network. However, in many domains, such as finance, neuroscience or climate studies, the observed dynamics does not follow the strict causality assumption. Thus, many reconstruction methods that try to deal with non-strictly causal dynamics have been developed in the last decade. These methods, however, tend to put limiting assumptions on the graph structures of networks. Furthermore, as we show in this dissertation, the network reconstruction problem is not well-posed in the non-strictly causal case, as in general, it does not admit a unique solution. Thus, many of the existing methods also do not provide any theoretical guarantees for the learned network. In this work, we develop network reconstruction methods for a large class of networks with non-strictly causal dynamics. We provide theoretical guarantees for the reconstruction, while posing no limitations on the underlying structure. We also address the ill-posedness of the network reconstruction problem. The only required assumption in the novel methods is that at least one strictly causal operator is present in every feedback loop. We also provide orientation rules that can orient some of the non-strictly causal links in the network. We test the methods on several benchmark examples, on random networks via simulations, and we also apply them on real-world datasets that show the effectiveness of the proposed algorithms.
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