Hierarchical explanation of inference in Bayesian networks that represent a population of independent agents

This paper describes a novel method for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak detection, in which the agents are patients who are modeled as independent, conditioned on the factors that cause disease spread. Given evidence about these patients, such as their symptoms, suppose that the BN system infers that a respiratory anthrax outbreak is highly likely. A public-health official who received such a report would generally want to know why anthrax is being given a high posterior probability. This paper describes the design of a system that explains such inferences. The explanation approach is applicable in general to inference in BNs that model conditionally independent agents; it complements previous approaches for explaining inference on BNs that model a single agent (e.g., explaining the diagnostic inference for a single patient using a BN that models just that patient).