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...
| Principais autores: | , , |
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| Outros Autores: | |
| Formato: | Artigo |
| Idioma: | en_US |
| Publicado em: |
Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Acesso em linha: | http://hdl.handle.net/1721.1/110806 https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0003-2658-8239 |