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1. I. Juva, P. Kuusela and J. Virtamo, A Case Study on Traffic Matrix Estimation Under Gaussian Distribution, in Seventeenth Nordic Teletraffic Seminar, Fornebu, Norway, 25 - 27 August, pp. 49 - 60, 2004 (bib)

   Abstract: We report a case study on an iterative method of traffic matrix estimation under some simplifying assumptions about the distribution of the origin-destination traffic demands. The starting point of our work is the Vaton-Gravey iterative Bayesian method, but there are quite a few differences between that method and our consideration. It is assumed that the distribution of the demands follow a single Gaussian distribution instead of being a modulated process. The normality assumption allows us to bypass the Markov Chain Monte Carlo step in the iterative method and explicitly derive the expected values for mean and covariance matrix of the traffic demands conditioned on link counts. We show that under the assumption of single underlying distribution the expected values of the mean and covariance converge after the first step of the iteration. This method cannot improve on this if no relation between mean and variance is imposed in order to make use of the covariance matrix estimates, or the distribution is assumed to be modulated from a regime of distributions.