  Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for
addressing both forward and inverse uncertainty quantification problems. In
this approach, QOI (quantity of interest) models are constrained by related
experimental observations with interval uncertainty. A collection of such
models and observations is termed a dataset and carves out a feasible region in
the parameter space. If a dataset has a nonempty feasible set, it is said to be
consistent. In real-world applications, it is often the case that collections
of experiments and observations are inconsistent. Revealing the source of this
inconsistency, i.e., identifying which models and/or observations are
problematic, is essential before a dataset can be used for prediction. To
address this issue, we introduce a constraint relaxation-based approach,
entitled the vector consistency measure, for investigating datasets with
numerous sources of inconsistency. The benefits of this vector consistency
measure over a previous method of consistency analysis are demonstrated in two
realistic gas combustion examples.
