The value of temporal data for learning of influence networks: A characterization via Kullback-Leibler divergence

The value of temporal data for learning of influence networks: A characterization via Kullback-Leibler divergence

We infer local influence relations between networked entities from data on outcomes and assess the value of temporal data by formulating relevant binary hypothesis testing problems and characterizing the speed of learning of the correct hypothesis via the Kullback-Leibler divergence, under three dif...

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Bibliographic Details
Main Authors: Zoumpoulis, Spyros I. (Author), Dahleh, Munther A (Contributor), Tsitsiklis, John N (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Institute for Data, Systems, and Society (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2017-07-21T18:35:17Z.
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