<p>This PhD thesis consists of an introduction and five papers, dealing with statistical methods in phylogenetics.</p><p>A phylogenetic tree describes the evolutionary relationships among species assuming that they share a common ancestor and that evolution takes place in a tree like manner. Our aim is to reconstruct the evolutionary relationships from aligned DNA sequences.</p><p>In the first two papers we investigate two measures of confidence for likelihood based methods, bootstrap frequencies with Maximum Likelihood (ML) and Bayesian posterior probabilities. We show that an earlier claimed approximate equivalence between them holds under certain conditions, but not in the current implementations of the two methods.</p><p>In the following two papers the divergence times of the internal nodes are considered. The ML estimate of the divergence time of the root is improved if longer sequences are analyzed or if more taxa are added. We show that the gain in precision is faster with longer sequences than with more taxa. We also show that the algorithm of the software package PATHd8 may give biased estimates if the global molecular clock is violated. A change of the algorithm to obtain unbiased estimates is therefore suggested.</p><p>The last paper deals with non-informative priors when using the Bayesian approach in phylogenetics. The term is not uniquely defined in the literature. We adopt the idea of data translated likelihoods and derive the so called Jeffreys' prior for branch lengths using Jukes Cantor model of evolution.</p>

This PhD thesis consists of an introduction and five papers, dealing with statistical methods in phylogenetics. A phylogenetic tree describes the evolutionary relationships among species assuming that they share a common ancestor and that evolution takes place in a tree like manner. Our aim is to reconstruct the evolutionary relationships from aligned DNA sequences. In the first two papers we investigate two measures of confidence for likelihood based methods, bootstrap frequencies with Maximum Likelihood (ML) and Bayesian posterior probabilities. We show that an earlier claimed approximate equivalence between them holds under certain conditions, but not in the current implementations of the two methods. In the following two papers the divergence times of the internal nodes are considered. The ML estimate of the divergence time of the root is improved if longer sequences are analyzed or if more taxa are added. We show that the gain in precision is faster with longer sequences than with more taxa. We also show that the algorithm of the software package PATHd8 may give biased estimates if the global molecular clock is violated. A change of the algorithm to obtain unbiased estimates is therefore suggested. The last paper deals with non-informative priors when using the Bayesian approach in phylogenetics. The term is not uniquely defined in the literature. We adopt the idea of data translated likelihoods and derive the so called Jeffreys' prior for branch lengths using Jukes Cantor model of evolution.