Segregating event streams and noise with a Markov renewal process model
  We describe an inference task in which a set of timestamped event
observations must be clustered into an unknown number of temporal sequences
with independent and varying rates of observations. Various existing approaches
to multi-object tracking assume a fixed number of sources and/or a fixed
observation rate; we develop an approach to inferring structure in timestamped
data produced by a mixture of an unknown and varying number of similar Markov
renewal processes, plus independent clutter noise. The inference simultaneously
distinguishes signal from noise as well as clustering signal observations into
separate source streams. We illustrate the technique via a synthetic experiment
as well as an experiment to track a mixture of singing birds.
