Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Schemes for fault identification in communication networks
IEEE/ACM Transactions on Networking (TON)
A coding approach to event correlation
Proceedings of the fourth international symposium on Integrated network management IV
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
A scheme for approximating probabilistic inference
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Why is diagnosis using belief networks insensitive to imprecision in probabilities?
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Recovering latent time-series from their observed sums: network tomography with particle filters.
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2007 workshop on Service-oriented computing performance: aspects, issues, and approaches
Active probing strategies for problem diagnosis in distributed systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Efficient stochastic local search for MPE solving
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
A model-based active testing approach to sequential diagnosis
Journal of Artificial Intelligence Research
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This paper studies the accuracy/efficiency trade-off in probabilistic diagnosis formulated as finding the most-likely explanation (MPE) in a Bayesian network. Our work is motivated by a practical problem of efficient real-time fault diagnosis in computer networks using test transactions, or probes, sent through the network. The key efficiency issues include both the cost of probing (e.g., the number of probes), and the computational complexity of diagnosis, while the diagnostic accuracy is crucial for maintaining high levels of network performance. Herein, we derive a lower bound on the diagnostic accuracy that provides necessary conditions for the number of probes needed to achieve an asymptotically error-free diagnosis as the network size increases, given prior fault probabilities and a certain level of noise in probe outcomes. Since the exact MPE diagnosis is generally intractable in large networks, we investigate next the accuracy/efficiency trade-offs for very simple and efficient local approximation techniques, based on variable-elimination (the mini-bucket scheme). Our empirical studies show that these approximations "degrade gracefully" with noise and often yield an optimal solution when noise is low enough, and our initial theoretical analysis explains this behavior for the simplest (greedy) approximation. These encouraging results suggest the applicability of such approximations to certain almost-deterministic diagnostic problems that often arise in practical applications.