Stochastic Complexity for tree models

We study the problem of data compression, gambling and prediction of strings xⁿ = x₁x₂...x_n 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.