One plus two may not equal two plus one in a social sensing network with unknown parameters

Stefano Maranò; Vincenzo Matta; Peter Willett

ICASSP 2016accepted0 citations

One plus two may not equal two plus one in a social sensing network with unknown parameters

Stefano Maranò, Vincenzo Matta, Peter Willett

Abstract

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.

BibTeX

@inproceedings{icassp2016_oneplustwomaynot,
  title = {One plus two may not equal two plus one in a social sensing network with unknown parameters},
  author = {Stefano Maranò and Vincenzo Matta and Peter Willett},
  booktitle = {ICASSP 2016},
  year = {2016}
}