A conditional independence framework for coherent modularized inference
Manuele Leonelli, Martine Jayne Barons, Jim Q. Smith · arXiv (Cornell University) · 2018
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.