  The context tree source is a source model in which the occurrence probability
of symbols is determined from a finite past sequence, and is a broader class of
sources that includes i.i.d. and Markov sources. The proposed source model in
this paper represents that a subsequence in each interval is generated from a
different context tree model. The Bayes code for such sources requires
weighting of the posterior probability distributions for the change patterns of
the context tree source and for all possible context tree models. Therefore,
the challenge is how to reduce this exponential order computational complexity.
In this paper, we assume a special class of prior probability distribution of
change patterns and context tree models, and propose an efficient Bayes coding
algorithm whose computational complexity is the polynomial order.
