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.

翻译：Aoristic 数据可以用一个标志性的时点过程来描述,在这个时点中,无法直接观察到这些点,但已知这些点处于可观察的间隔期,即标记。我们考虑巴伊西亚州对潜在点的估计,当标记以平衡的交替更新过程为模型,而前者是Markov点过程。我们得出后端分布,估计其参数,并举一些例子来说明先前分布的影响。