Maximal Accurate Forests from Distance Matrices

We present a fast converging method for distance-based phylogenetic inference, which is novel in two respects. First, it is the only method (to our knowledge) to guarantee accuracy when knowledge about the model tree, i.e bounds on the edge lengths, is not assumed. Second, our algorithm guarantees that, with high probability, no false assertions are made. The algorithm produces a maximal forest of the model tree, in time e O n in the typical case. Empirical testing has been promising, comparing favorably to Neighbor Joining, with the advantage of making few or no false assertions about the topology of the model tree; guarantees against false positives can be controlled as a parameter by the user.