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arXiv Machine Learning By Christopher Meek, Kayvan Sadeghi

Characterizing and Identifying Separable Graphical Models

Read the original on arXiv Machine Learning →

arXiv:2607. 01057v1 Announce Type: cross Abstract: We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirected edges, that are capable of encoding independence structures arising from feedback, latent and selection mechanisms.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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