A conditional independence framework for coherent modularized inference
Methodology
2018-07-30 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1807.10628,
title = {A conditional independence framework for coherent modularized inference},
author = {Manuele Leonelli and Martine J. Barons and Jim Q. Smith},
journal= {arXiv preprint arXiv:1807.10628},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1507.07394