Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
An evaluation of explanations of probabilistic inference
Computers and Biomedical Research - Papers presented at the 16th symposium on computer applications in medical care (SCAMC)
Applying Bayesian networks to information retrieval
Communications of the ACM
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Bayesian biosurveillance of disease outbreaks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Towards explanation of scientific and technological emergence
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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).