State estimation for aoristic models
研究了aoristic数据（事件发生时间未知但落在已知区间内）的贝叶斯状态估计方法，推导了后验分布并估计参数，通过例子说明先验分布的影响，并应用于估计区间删失犯罪事件的发生时间。
Abstract 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 modeled in terms of an alternating renewal process in equilibrium and the prior is a Markov point process. We derive the posterior distribution, estimate its parameters and present some examples that illustrate the influence of the prior distribution. The model is then used to estimate times of occurrence of interval censored crimes.