2018/07/26 by Manuele Leonelli, Martine J. Barons, Leonelli, Manuele +3
Computer Science · Engineering · #Bayesian Modeling and Causal Inference #Fault Detection and Control Systems #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1807.10628
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.