10 citations · 21 across the 4 of their papers we have counts for
4 papers
Bayesian inference for continuous-time hidden Markov models with an unknown number of states
Yu Luo, David A. Stephens
We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number…
Non-parametric Bayesian inference via loss functions under model misspecification
Yu Luo, David A. Stephens, Daniel J. Graham +1
In the usual Bayesian setting, a full probabilistic model is required to link the data and parameters, and the form of this model and the inference and prediction mechanisms are sp…
Bayesian Clustering for Continuous-Time Hidden Markov Models
Yu Luo, David A. Stephens, David L. Buckeridge
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically in th…
Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models
Aman Verma, Guido Powell, Yu Luo +2
Modeling disease progression in healthcare administrative databases is complicated by the fact that patients are observed only at irregular intervals when they seek healthcare serv…