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Project: Algorithms for reasoning in credal trees

project duration
01-OCT-11 – 30-SEP-13
abstract
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.