State estimation for aoristic models

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