Bayesian dynamic probit models for the analysis of longitudinal data
A dynamic probit model with a first order Markov process was developed. Gibbs sampler using data augmentation approach and forward filtering backward sampling algorithm were presented. The discussion was extended to propose models to generalized models including Student t t -link function. Model fit was compared between static and dynamic probit models.
longitudinal data markov models bayesian inference dynamic linear models model selection