2021
DOI: 10.48550/arxiv.2109.10642
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Decentralized Learning of Tree-Structured Gaussian Graphical Models from Noisy Data

Abstract: This paper studies the decentralized learning of tree-structured Gaussian graphical models (GGMs) from noisy data. In decentralized learning, data set is distributed across different machines (sensors), and GGMs are widely used to model complex networks such as gene regulatory networks and social networks. The proposed decentralized learning uses the Chow-Liu algorithm for estimating the treestructured GGM.In previous works, upper bounds on the probability of incorrect tree structure recovery were given mostly… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 25 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?