Develop theory and efficient algorithms for inferences in credal trees, with emphasis on imprecise hidden Markov models (iHMM). These represent a system’s uncertain evolution through states, where we can only observe the states imperfectly, through uncertain outputs. I plan to adress: learning an iHMM model from sequences of observations, dealing with missing data and extending results to general credal trees.