End-to-end internet packet dynamics
IEEE/ACM Transactions on Networking (TON)
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Robust Identification of Shared Losses Using End-to-End Unicast Probes
Robust Identification of Shared Losses Using End-to-End Unicast Probes
Multicast-based inference of network-internal loss characteristics
IEEE Transactions on Information Theory
Software failure prediction based on a Markov Bayesian network model
Journal of Systems and Software
Bayesian network based software reliability prediction with an operational profile
Journal of Systems and Software
A bottom-up inference of loss rate
Computer Communications
BC_EM: a link loss inference algorithm for wireless sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Loss tomography in wireless sensor network using Gibbs sampling
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
A distributed approach to estimate link-level loss rates
ICA3PP'05 Proceedings of the 6th international conference on Algorithms and Architectures for Parallel Processing
Bottom up algorithm to identify link-level transition probability
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
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Network tomography aims to obtain link-level performance characteristics, such as loss rate and average delay on each link, by end-to-end measurement. The obtained information can help us to understand the dynamic nature of networks. A number of methods have been proposed in recent years, which can be divided into two classes: multicast-based and unicast-based. In this paper, we propose an approach in the multicast class that uses the Bayesian network to carry out statistical inference. Simulations based on the network simulator 2 (ns2) were conducted, which shows our approach produced almost identical result as that produced by the maximum likelihood estimator previous proposed.