  Aoristic data can be described by a marked point process in time in which the
points cannot be observed directly but are known to lie in observable
intervals, the marks. We consider Bayesian state estimation for the latent
points when the marks are modelled in terms of an alternating renewal process
in equilibrium and the prior is a Markov point point process. We derive the
posterior distribution, estimate its parameters and present some examples that
illustrate the influence of the prior distribution.
