  Robust belief revision methods are crucial in streaming data situations for
updating existing knowledge or beliefs with new incoming evidence. Bayes
conditioning is the primary mechanism in use for belief revision in data fusion
systems that use probabilistic inference. However, traditional conditioning
methods face several challenges due to inherent data/source imperfections in
big-data environments that harness soft (i.e., human or human-based) sources in
addition to hard (i.e., physics-based) sensors. The objective of this paper is
to investigate the most natural extension of Bayes conditioning that is
suitable for evidence updating in the presence of such uncertainties. By
viewing the evidence updating process as a thought experiment, an elegant
strategy is derived for robust evidence updating in the presence of extreme
uncertainties that are characteristic of big-data environments. In particular,
utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes
conditioning is derived for evidence that takes the form of a general belief
function. The presented work differs fundamentally from the Conditional Update
Equation (CUE) and authors own extensions of it. An overview of this
development is provided via illustrative examples. Furthermore, insights into
parameter selection under various fusion contexts are also provided.
