Exact Inference for Continuous Time Markov Chain Models
提出在均匀扩散先验下对连续时间齐次马尔可夫链模型进行精确贝叶斯推断的方法，通过蒙特卡洛积分计算任意感兴趣函数的后验分布，并自然处理了可嵌入性问题。
Methods for exact Bayesian inference under a uniform diffuse prior are set forth for the continuous time homogeneous Markov chain model. It is shown how the exact posterior distribution of any function of interest may be computed using Monte Carlo integration. The solution handles the problems of embeddability in a very natural way, and provides (to our knowledge) the only solution that systematically takes this problem into account. The methods are illustrated using several sets of data.