Stochastic Complexity for tree models

Jun'Ichi Takeuchi, Andrew R. Barron

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


We study the problem of data compression, gambling and prediction of strings xn = x1x2...xn in terms of coding regret, where the tree model is assumed as a target class. We apply the minimax Bayes strategy for curved exponential families to this problem and show that it achieves the minimax regret without restriction on the data strings. This is an extension of the minimax result by (Takeuchi et al. 2013) for models of kth order Markov chains and determines the constant term of the Stochastic Complexity for the tree model.

Original languageEnglish
Title of host publication2014 IEEE Information Theory Workshop, ITW 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479959990
Publication statusPublished - Dec 1 2014
Event2014 IEEE Information Theory Workshop, ITW 2014 - Hobart, Australia
Duration: Nov 2 2014Nov 5 2014

Publication series

Name2014 IEEE Information Theory Workshop, ITW 2014


Other2014 IEEE Information Theory Workshop, ITW 2014

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications


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