  Inference in current domains of application are often complex and require us
to integrate the expertise of a variety of disparate panels of experts and
models coherently. In this paper we develop a formal statistical methodology to
guide the networking together of a diverse collection of probabilistic models.
In particular, we derive sufficient conditions that ensure inference remains
coherent across the composite before and after accommodating relevant evidence.
