Inverse Marginalisation for Safely Expanding Bayesian Networks

Abstract

After clinical decision support systems are validated and deployed, one is often reluctant to update the model with new insights or data, especially if this means that re-certification is required. In this paper we address this issue in updating Bayesian networks with new domain knowledge. More specifically, we introduce and study the concept of safe inverse marginalisation, an operation that allows for adding new variables to a network without affecting the distribution over the original variables. As such, the additional efforts required for validation and certification can be limited, re-using as much as possible the analyses and documentation from the original model. To support the process of safely extending a Bayesian network, we present an algorithm that flags potentially unsafe updates.