Trust Estimation of Sources Over Correlated Propositions

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.