Letter to the Editor Bayesian Probabilities and Quartet Puzzling
نویسندگان
چکیده
Quartet puzzling (QP), a heuristic tree search procedure for maximum-likelihood trees, has recently been introduced (Strimmer and von Haeseler 1996). This method uses maximum-likelihood criteria for quartets of taxa which are then combined to form trees based on larger numbers of taxa. Thus, QP can be practically applied to data sets comprising a much greater number of taxa than can other search algorithms such as stepwise addition and subsequent branch swapping as implemented, e.g., in DNAML (Felsenstein 1993). However, its ability to reconstruct the true tree is less than that of DNAML (Strimmer and von Haeseler 1996). Here, we show that the assignment of penalties in the puzzling step of the QP algorithm is a special case of a more general Bayesian weighting scheme for quartet topologies. Application of this general framework leads to an improvement in the efficiency of QP at recovering the true tree as well as to better theoretical understanding of the method itself. On average, the accuracy of QP increases by 10% over all cases studied, without compromising speed or requiring more computer memory. Consider the three different fully-bifurcating tree topologies Q,, Q2, and Q3 for four taxa (fig. 1). Denote by ml, m2, and m3 their corresponding maximum-likelihood (not log-likelihood) values. Note that ml + m2 + m3 << 1. Evaluation via Bayes’ theorem of the three tree topologies given uniform prior information leads to posterior probabilities
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تاریخ انتشار 1998