Fast Statistical Alignment

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated f...

Fast Statistical Alignment : Text S 1

Boosting Statistical Word Alignment

This paper proposes an approach to improve statistical word alignment with the boosting method. Applying boosting to word alignment must solve two problems. The first is how to build the reference set for the training data. We propose an approach to automatically build a pseudo reference set, which can avoid manual annotation of the training set. The second is how to calculate the error rate of...

Improved Statistical Alignment Models

Fast optimal alignment

We show how to speed up sequence alignment algorithms of the type introduced by Needleman and Wunsch (and generalized by Sellers and others). Faster alignment algorithms have been introduced, but always at the cost of possibly getting sub-optimal alignments. Our modification results in the optimal alignment still being found, often in 1/10 the usual time. What we do is reorder the computation o...