  We introduce a new type of graphical model that we call a "memory factor
network" (MFN). We show how to use MFNs to model the structure inherent in many
types of data sets. We also introduce an associated message-passing style
algorithm called "proactive message passing"' (PMP) that performs inference on
MFNs. PMP comes with convergence guarantees and is efficient in comparison to
competing algorithms such as variants of belief propagation. We specialize MFNs
and PMP to a number of distinct types of data (discrete, continuous, labelled)
and inference problems (interpolation, hypothesis testing), provide examples,
and discuss approaches for efficient implementation.
