  We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a
cyberinfrastructure that we are developing which will unify the treatment of
nuclear models, experimental data, and associated uncertainties. We overview
the statistical principles and nuclear-physics contexts underlying the BAND
toolset, with an emphasis on Bayesian methodology's ability to leverage insight
from multiple models. In order to facilitate understanding of these tools we
provide a simple and accessible example of the BAND framework's application.
Four case studies are presented to highlight how elements of the framework will
enable progress on complex, far-ranging problems in nuclear physics. By
collecting notation and terminology, providing illustrative examples, and
giving an overview of the associated techniques, this paper aims to open paths
through which the nuclear physics and statistics communities can contribute to
and build upon the BAND framework.
