Improvement of the inside-outside algorithm using prediction and application to RNA modeling

In the last decade, stochastic grammars became an important probabilistic tool for modeling biological sequences. The inside-outside (IO) algorithm is the most popular estimator for learning the probability parameters associated to the rules from training sequences. The inside-outside algorithm needs O(L/sup 3/M/sup 3/) (L is the length of training sequence and M is the number of non-terminals) operations. The IO algorithm considers a number of useless inside variables (/spl alpha/-variables) and useless outside variables (/spl beta/-variables) in each training iteration. In this paper we give a method to avoid these useless variables. Our method uses a prediction function for this purpose. We give an example based on RNA sequences (taken from [Y. Sakakibara, et al., 1994]) to illustrate the percentage of such useless variables avoided in our method.