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Performance of real-time applications on end-to-end packet 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 modeled by a hidden Markov model with appropriate hidden variables that capture the current state of the network. In this paper we propose an input/output hidden Markov model that, trained with a modified version of the expectation-maximization algorithm, shows excellent performance in modeling typical channel behaviors in a set of real packet links. The work extends to case of variable inter-departure time the previous proposed hidden Markov model that well characterizes losses and delays of packets from a periodic source.