Bootstrap- This is a method of attempting to estimate confidence levels of inferred relationships. The bootstrap proceeds by resampling the original data matrix with replacement of the characters. It is analagous to cutting the data matrix into individual columns of data and throwing the characters into a hat. A character is then drawn at random from this hat and it becomes the first character of the new datamatrix. The character is then replaced in the hat, the hat is shaken and again another character is drawn from the hat. This process is repeated until our new pseudoreplicate is the same size as the original. Some characters will be sampled more than once and some will not be sampled at all. This process is repeated many times (say, 100-1,000) and phylogenies are reconstructed each time. After the bootstrap procedure is finished, a majority-rule consensus tree is constructed from the optimal tree from each bootstrap sample. The bootstrap support for any internal branch is the number of times it was recovered during the bootstrapping procedure. This method is related to jacknifing.
This is a method of searching through tree space in order to find optimal
methods, this is a heuristic and
will ignore families of trees that cannot possibly give a better answer
than a tree that already been found. It is much faster than exhaustive
searches, but remains impractical for large numbers of taxa (say, more
Bush- see Star topology.
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