  This work introduces a comprehensive approach to assess the sensitivity of
model outputs to changes in parameter values, constrained by the combination of
prior beliefs and data. This novel approach identifies stiff parameter
combinations strongly affecting the quality of the model-data fit while
simultaneously revealing which of these key parameter combinations are informed
primarily by the data or are also substantively influenced by the priors. We
focus on the very common context in complex systems where the amount and
quality of data are low compared to the number of model parameters to be
collectively estimated, and showcase the benefits of this technique for
applications in biochemistry, ecology, and cardiac electrophysiology. We also
show how stiff parameter combinations, once identified, uncover controlling
mechanisms underlying the system being modeled and inform which of the model
parameters need to be prioritized in future experiments for improved parameter
inference from collective model-data fitting.
