The lifecycle of Bayesian network models developed for Multi-Source Signature assessment of Nuclear Programs

Abstract

The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory's (PNNL's) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information to assess nuclear activities through a series of Bayesian network (BN) models. Information types may include text, sensor output, imagery, or audio/video files. The BN models incorporate knowledge from a diverse range of information sources to help assess a country's nuclear activities. The models span engineering topic areas, country-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country's likelihood to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model helps non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.