Trust estimation of sources over correlated propositions

Name: Trust estimation of sources over correlated propositions
Author: Kaplan, L., Şensoy, Murat

İsim	Trust estimation of sources over correlated propositions
Yazar	Kaplan, L., Şensoy, Murat
Basım Tarihi:	2018-09-05
Basım Yeri	- IEEE
Konu	Estimation, Uncertainty, Probabilistic logic, Correlation, Government, History, Fuses
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-0-9964-5276-2
Kayıt Numarası	fe49eb07-b77b-4766-806b-b9c2299f4d89
Lokasyon	Computer Science
Tarih	2018-09-05
Notlar	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
Örnek Metin	This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are statistically independent. A new source behavior estimation methods is presented that accounts for statistical dependencies between the training propositions. Simulations seem to indicate that the potential performance gains for accounting for the correlations is small relative to the increased computational complexity. One may conclude that the traditional independence assumption in source behavior estimation methods is reasonable even in cases where it is actually violated.
DOI	10.23919/ICIF.2018.8455400

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Trust estimation of sources over correlated propositions

Yazar Kaplan, L., Şensoy, Murat

Basım Tarihi 2018-09-05

Basım Yeri - IEEE

Konu Estimation, Uncertainty, Probabilistic logic, Correlation, Government, History, Fuses

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-0-9964-5276-2

Kayıt Numarası fe49eb07-b77b-4766-806b-b9c2299f4d89

Lokasyon Computer Science

Tarih 2018-09-05

Notlar United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence

Örnek Metin This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are statistically independent. A new source behavior estimation methods is presented that accounts for statistical dependencies between the training propositions. Simulations seem to indicate that the potential performance gains for accounting for the correlations is small relative to the increased computational complexity. One may conclude that the traditional independence assumption in source behavior estimation methods is reasonable even in cases where it is actually violated.

DOI 10.23919/ICIF.2018.8455400