Concerning the NJ algorithm and its unweighted version, UNJ

@inproceedings{Gascuel1996ConcerningTN,
  title={Concerning the NJ algorithm and its unweighted version, UNJ},
  author={Olivier Gascuel},
  booktitle={Mathematical Hierarchies and Biology},
  year={1996}
}
In this paper we will present UNJ, an unweighted version of the NJ algorithm (Saitou and Nei 1987; Studier and Keppler 1988). We will demonstrate that UNJ is well suited when the data are of the ( ) ( ) δ ε ij ij ij d = + type, where ( ) dij is a tree distance, and when the εij are independent and identically distributed noise variables. Simulations confirm this theory. On a more general level, we will study the three main components of the agglomerative approach, applied to the reconstruction… CONTINUE READING

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