The Topological Fusion of Bayes Nets
This addresses the need for compromise in collaborative Bayesian modeling, though it appears incremental as it extends prior work to a specific setting.
The paper tackles the problem of achieving consensus in multiple-author Bayesian networks by introducing prior compromises, and it develops an efficient algorithm for fusing two directed acyclic graphs into a single consensus structure.
Bayes nets are relatively recent innovations. As a result, most of their theoretical development has focused on the simplest class of single-author models. The introduction of more sophisticated multiple-author settings raises a variety of interesting questions. One such question involves the nature of compromise and consensus. Posterior compromises let each model process all data to arrive at an independent response, and then split the difference. Prior compromises, on the other hand, force compromise to be reached on all points before data is observed. This paper introduces prior compromises in a Bayes net setting. It outlines the problem and develops an efficient algorithm for fusing two directed acyclic graphs into a single, consensus structure, which may then be used as the basis of a prior compromise.