The value of temporal data for learning of influence networks: A characterization via Kullback-Leibler divergence
Name
The value of temporal data.pdf
Size
613.64 KB
Format
Adobe PDF
Checksum (MD5)
e55b0f78cb99dbfa4379f292f50d115c
Author(s)
Zoumpoulis, Spyros I.
Dahleh, Munther A
Tsitsiklis, John N
Date Issued
February 2016
Journal
2015 54th IEEE Conference on Decision and Control (CDC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dahleh, Munther A., John N. Tsitsiklis, and Spyros I. Zoumpoulis. “The Value of Temporal Data for Learning of Influence Networks: A Characterization via Kullback-Leibler Divergence.” 2015 IEEE 54th Annual Conference on Decision and Control (CDC), Osaka, Japan, 15-18 December, 2015. IEEE, 2015. 2907–2912.
Version
Author's final manuscript
Abstract
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 different types of available data: knowing the set of entities who take a particular action; knowing the order that the entities take an action; and knowing the times of the actions.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC.2015.7402658
