TY - GEN
T1 - Stochastic Complexity for tree models
AU - Takeuchi, Jun'Ichi
AU - Barron, Andrew R.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/1
Y1 - 2014/12/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84929316288
UR - https://www.scopus.com/pages/publications/84929316288#tab=citedBy
U2 - 10.1109/ITW.2014.6970825
DO - 10.1109/ITW.2014.6970825
M3 - Conference contribution
AN - SCOPUS:84929316288
T3 - 2014 IEEE Information Theory Workshop, ITW 2014
SP - 222
EP - 226
BT - 2014 IEEE Information Theory Workshop, ITW 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Information Theory Workshop, ITW 2014
Y2 - 2 November 2014 through 5 November 2014
ER -