Bayesian inference for continuous-time hidden Markov models with an unknown number of states.

ABSTRACT: 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 of states has to be pre-specified, and Bayesian inference for a fixed number of states has not been studied until recently. In addition, although approaches to address the problem for discrete-time models have been developed, no method has been successfully implemented for the continuous-time case. We focus on reversible jump Markov chain Monte Carlo which allows the trans-dimensional move among different numbers of states in order to perform Bayesian inference for the unknown number of states. Specifically, we propose an efficient split-combine move which can facilitate the exploration of the parameter space, a

SUBMITTER: Luo Y 

PROVIDER: S-EPMC8550639 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature