In this poster a Bayesian estimation framework for a non-stationary Markov model is developed for situations where sample data with observed transition between classes (micro data) and aggregate population shares (macro data) are available. Posterior distributions on transition probabilities are derived based on a micro based prior and a macro based Likelihood function thereby consistently combining previously separated approaches. Monte Carlo simulations for ordered and unordered Markov states show how observed micro transitions improve precision of posterior knowledge as the sample size increases.