Bayes estimators for phylogenetic reconstruction.

Abstract:

:Tree reconstruction methods are often judged by their accuracy, measured by how close they get to the true tree. Yet, most reconstruction methods like maximum likelihood (ML) do not explicitly maximize this accuracy. To address this problem, we propose a Bayesian solution. Given tree samples, we propose finding the tree estimate that is closest on average to the samples. This "median" tree is known as the Bayes estimator (BE). The BE literally maximizes posterior expected accuracy, measured in terms of closeness (distance) to the true tree. We discuss a unified framework of BE trees, focusing especially on tree distances that are expressible as squared euclidean distances. Notable examples include Robinson-Foulds (RF) distance, quartet distance, and squared path difference. Using both simulated and real data, we show that BEs can be estimated in practice by hill-climbing. In our simulation, we find that BEs tend to be closer to the true tree, compared with ML and neighbor joining. In particular, the BE under squared path difference tends to perform well in terms of both path difference and RF distances.

journal_name

Syst Biol

journal_title

Systematic biology

authors

Huggins PM,Li W,Haws D,Friedrich T,Liu J,Yoshida R

doi

10.1093/sysbio/syr021

subject

Has Abstract

pub_date

2011-07-01 00:00:00

pages

528-40

issue

4

eissn

1063-5157

issn

1076-836X

pii

syr021

journal_volume

60

pub_type

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