A Markov model of land use dynamics
  The application of the Markov chain to modeling agricultural succession is well known. In most cases, the main problem is the inference of the model, i.e. the estimation of the transition matrix. In this work we present methods to estimate the transition matrix from historical observations. In addition to the estimator of maximum likelihood (MLE), we also consider the Bayes estimator associated with the Jeffreys prior. This Bayes estimator will be approximated by a Markov chain Monte Carlo (MCMC) method. We also propose a method based on the sojourn time to test the adequation of Markov chain model to the dataset. 
Fabien Campillo (INRIA Sophia Antipolis, MISTEA), Dominique Herv\'e   (GRED), Angelo Raherinirina, Rivo Rakotozafy
math.PR