  The inferential model (IM) approach, like fiducial and its generalizations,
depends on a representation of the data-generating process. Here, a particular
variation on the IM construction is considered, one based on generalized
associations. The resulting generalized IM is more flexible than the basic IM
in that it does not require a complete specification of the data-generating
process and is provably valid under mild conditions. Computation and
marginalization strategies are discussed, and two applications of this
generalized IM approach are presented.
