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The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities.
Data produced by two different sources is classified using variable length Markov chains. In many realistic situations it is conceiveable that the probabilistic context trees corresponding to the two ...
We introduce a new criterion to select in a consistent way the probabilistic context tree generating a sample. The basic idea is to construct a totally ordered set of candidate trees. This set is co...
We find upper bounds for the probability of underestimation and overestimation errors in penalized likelihood context tree estimation. The bounds are explicit and applies to processes of not necessa...

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