Data compression with finite windows
Communications of the ACM
The context-tree weighting method: extensions
IEEE Transactions on Information Theory
An O(N) semipredictive universal encoder via the BWT
IEEE Transactions on Information Theory
Linear time universal coding and time reversal of tree sources via FSM closure
IEEE Transactions on Information Theory
Consistency of the Unlimited BIC Context Tree Estimator
IEEE Transactions on Information Theory
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
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The context tree models are widely used in a lot of research fields. Patricia[7] like trees are applied to the context trees that are expanded according to the increase of the length of a source sequence in the previous researches of nonpredictive source coding and model selection. The space complexity of the Patricia like context trees are O(t) where t is the length of a source sequence. On the other hand, the predictive Bayes source coding algorithm cannot use a Patricia like context tree, because it is difficult to hold and update the posterior probability parameters on a Patricia like tree. So the space complexity of the expanded trees in the predictive Bayes coding algorithm is O(t2). In this paper, we propose an efficient predictive Bayes coding algorithm using a new representation of the posterior probability parameters and the compact context tree holding the parameters whose space complexity is O(t).