  We present a generative model for representing and reasoning about the
relationships among events in continuous time. We apply the model to the domain
of networked and distributed computing environments where we fit the parameters
of the model from timestamp observations, and then use hypothesis testing to
discover dependencies between the events and changes in behavior for monitoring
and diagnosis. After introducing the model, we present an EM algorithm for
fitting the parameters and then present the hypothesis testing approach for
both dependence discovery and change-point detection. We validate the approach
for both tasks using real data from a trace of network events at Microsoft
Research Cambridge. Finally, we formalize the relationship between the proposed
model and the noisy-or gate for cases when time can be discretized.
