Bayesian Modeling for Packet Channels

Performance of real-time applications on network communication channels are strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and present a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modelled by a Dynamic Bayesian Network with an appropriate hidden variable that captures the current state of the network. In this paper we propose a Bayesian model that, trained with a version of the EM-algorithm, seems to be effective in modelling typical channel behaviors.