Accuracies of the simple methods for estimating the bootstrap probability of a maximum-likelihood tree

Abstract

Two simple methods have been proposed by Kishino et al. for estimating bootstrap probabilities of alternative trees from a maximum-likelihood analysis without performing maximum-likelihood estimation for each resampled data set; i.e., a resampling estimated log-likelihood method and a multivariate normal distribution method. To examine the extent to which these two methods provide good approximations, bootstrap probabilities estimated by these methods from real data were compared with those estimated by repeated bootstrap resampling and maximum-likelihood estimation. It turned out that both of these simple methods are good approximations to the computationally intensive bootstrap method. This finding should motivate people in this field to use the maximum-likelihood method when inferring molecular evolutionary trees.