Journal of Biomedical Informatics
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Agent-encapsulated Bayesian networks and the rumor problem
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Knowledge representation, communication, and update in probability-based multiagent systems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Soft evidential update via Markov chain Monte Carlo inference
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Exploiting the probability of observation for efficient bayesian network inference
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffrey's rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods. This indepth understanding leads us to propose two algorithms for belief update with multiple soft evidences. Both of these algorithms can be seen as integrating the techniques of virtual evidence method, IPFP and traditional BN evidential inference, and they have clear computational and practical advantages over the methods proposed by others in the past.