Subtree Pruning and Regrafting (SPR) topological rearrangements are usually sufficient
to intensively search the tree space. Here, we propose two new methods to make SPR moves
more efficient. The first method uses a fast distance-based approach to detect the least
promising candidate SPR moves, which are then simply discarded. The second method locally
estimates the change in likelihood for any remaining potential SPRs, as opposed to
globally evaluating the entire tree for each possible move. These two methods are
implemented in a new algorithm with a sophisticated filtering strategy, which efficiently
selects potential SPRs and concentrates most of the likelihood computation on the
promising moves.
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