Dirichlet process multi-state mixture models
提出一个贝叶斯非参数框架，用狄利克雷过程混合模型拟合连续时间多状态过程的离散观测数据，能灵活捕捉过程动态中的未观测异质性和非马尔可夫记忆效应，并通过模拟和真实数据验证有效性。
A Bayesian nonparametric framework is introduced for modeling discretely observed trajectories of continuous-time multi-state processes. By employing Dirichlet Process Mixtures with Markov, inhomogeneous Markov, and semi-Markov kernels, the approach flexibly captures unobserved heterogeneity in the process dynamics. Crucially, the mixture structure induces a generalized form of non-Markovianity, as future state predictions depend on the entire observed history through component-specific weighting. This allows the model to capture complex temporal dependencies and memory effects beyond the scope of traditional multi-state models. The effectiveness of the methodology is demonstrated through simulation studies and an application to a real data set.