Sequential Monte Carlo inference of internal delays in nonstationary data networks

IEEE Transactions on Signal Processing

Volumn 50, Issue 2, 2002, Pages 366-376

  Coates, Mark J ^a,b   Nowak, Robert D ^a,b  

^a IEEE   (United States)

^b Rice University   (United States)

Author keywords

Network tomography; Sequential Monte Carlo

Indexed keywords

NONSTATIONARY DATA NETWORKS; ROUTING;

ALGORITHMS; COMPUTER SIMULATION; DATA COMMUNICATION SYSTEMS; MONTE CARLO METHODS; NETWORK PROTOCOLS; ROUTERS; TELECOMMUNICATION TRAFFIC;

PACKET NETWORKS;

EID: 0036475633     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.978391     Document Type: Article

References (39)

1. Multicast-based inference of network-internal loss characteristics
2. Network loss inference using unicast end-to-end measurement
3. Networks for networks: Internet analysis using Bayesian graphical models
4. Network tomography for internal delay estimation
5. Inferring link loss using striped unicast probes
6. Robust identification of shared losses using end-to-end unicast probes
7. Inference of multicast routing trees and bottleneck bandwidths using end-to-end measurements
8. Passive network tomography using EM algorithms
9. Bayesian inference on network traffic using link count data (with discussion)
10. Time-varying network tomography: Router link data
11. Network tomography: Estimating source-destination traffic intensities from link data
12. A fast weighted Bayesian bootstrap filter for nonlinear model state estimation
13. Sequential estimation of signals under model uncertainty
14. Methodology for Monte Carlo smoothing with application to time-varying autoregressions
15. Monte Carlo filter and smoother for nonlinear non-Gaussian state space models
16. Sequential Monte Carlo methods for dynamic systems
17. Fixed-lag smoothing using sequential importance sampling
18. On sequential Monte Carlo sampling methods for Bayesian filtering
19. Monte Carlo techniques to estimate the conditional expectation in multi-stage nonlinear filtering
20. Novel approach to nonlinear/non-Gaussian Bayesian state estimation
21. Monte Carlo filter using the genetic algorithm operators
22. Monte Carlo approximation for general state space models
23. Monte Carlo smoothing for nonlinear time series
24. Monte Carlo technique in problems of optimal data processing
25. Combined parameter and state estimation in simulation-based filtering
26. Filtering via simulation: Auxiliary particle filters