GEN02-2: Pseudo Maximum Likelihood Loss Estimates for General Topologies

Loss tomography as a key component of network tomography receives considerable attention in recent years and a number of methods based on maximum likelihood estimate (MLE) and Bayesian estimate have been proposed. However, most methods proposed so far only target a tree-like network. In this paper, we derive a direct expression to unveil the correlation between observations and loss rates in a network with a general topology. Based on the expression, we propose four algorithms to estimate the loss rates of such a network: called modified weighted average, combine probe top down (CPTD), hybrid bottom up and top down (HBT) and MCMC. The four algorithms proposed in this paper are compared against the MVWA, which show that the CPTD and HBT are pseudo maximum likelihood estimates and more accurate than weight average algorithms. This finding is confirmed through simulations.