Parametric estimation for the generative social sensing model proposed in [19,20] is addressed. First, we provide a detailed analysis of the estimation performance bounds, in terms of the Fisher information matrix, with emphasis on the fundamental scaling laws as the number of network agents and/or the number of monitored agents' activities is large. Then, we examine two viable estimation procedures that can be useful even in such large dataset applications: the Expectation-Maximization and the Fisher scoring algorithms, which both achieve the aforementioned performance bounds.
|Titolo:||One plus two may not equal two plus one in a social sensing network with unknown parameters|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||4.1.2 Proceedings con ISBN|