Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider parametric estimation from social data, exploiting the generative social sensing model proposed in   [18] ,  [19] . First,  provide a detailed analysis of the estimation performance bounds, in terms of the Fisher information matrix, with emphasis on the regimes where the number of network agents and/or the number of monitored agents’ activities is large. Then,  examine the performance of two likelihood-based estimation algorithms, namely: the expectation–maximization algorithm, and the Fisher scoring method, which both achieve the aforementioned estimation performance bounds. The analysis, corroborated by the application to a couple of classical estimation problems, allows:    $\text{1)}$   highlighting the fundamental scaling laws for the considered parametric social sensing model;   $\text{2)}$    identifying viable algorithmic procedures that can be useful even in large dataset applications.
['Stefano Marano', 'Vincenzo Matta', 'Peter Willett']
I am writing a research paper on a idea The Importance of Being Earnest: Social Sensing With Unknown Agent Quality
[{"from": "human", "value": "I am writing a research paper on a idea The Importance of Being Earnest: Social Sensing With Unknown Agent Quality"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider parametric estimation from social data, exploiting the generative social sensing model proposed in\u00a0  [18] ,  [19] . First,  provide a detailed analysis of the estimation performance bounds, in terms of the Fisher information matrix, with emphasis on the regimes where the number of network agents and/or the number of monitored agents\u2019 activities is large. Then,  examine the performance of two likelihood-based estimation algorithms, namely: the expectation\u2013maximization algorithm, and the Fisher scoring method, which both achieve the aforementioned estimation performance bounds. The analysis, corroborated by the application to a couple of classical estimation problems, allows:    $\\text{1)}$   highlighting the fundamental scaling laws for the considered parametric social sensing model;   $\\text{2)}$    identifying viable algorithmic procedures that can be useful even in large dataset applications."}]