Drummond et al. (2006) introduce a Bayesian method for relaxed phylogenetics and dating with confidence. The method allows co-estimation of phylogeny and divergence times under a new class of relaxed-clock models. It is demonstrated using simulation and real datasets. The method uses probabilistic calibration priors instead of point calibrations to account for calibration uncertainties. The method is implemented in BEAST and is used to analyze 102 bacterial, 106 yeast, 61 plant, 99 metazoan, and 500 primate alignments. The results show that the method is more accurate and precise than traditional unrooted models while adding the ability to infer a timescale to evolution. The method also provides a measure of clocklikeness of datasets and compares this measure between different genes and phylogenies. The results suggest that autocorrelated models may not be suitable for some datasets. The method is also applied to viral and marsupial datasets, showing that the marsupial dataset is more clocklike than the viral datasets. The method is compared with other models, showing that the uncorrelated relaxed-clock model performs well in most cases. The method is also used to assess the accuracy and precision of phylogenetic inference using three Bayesian methods: CLOC, UCLN, and UF. The results show that the uncorrelated relaxed-clock model is the most accurate in most cases. The method is also used to assess the accuracy of phylogenetic inference in the context of model misspecification. The method is found to be more accurate and precise than traditional unrooted models. The method is also used to assess the impact of different prior distributions on phylogenetic inference. The method is found to be more accurate when the prior distribution is based on the data. The method is also used to assess the impact of different models of rate variation on phylogenetic inference. The method is found to be more accurate when the model of rate variation is based on the data. The method is also used to assess the impact of different models of substitution on phylogenetic inference. The method is found to be more accurate when the model of substitution is based on the data. The method is also used to assess the impact of different models of population dynamics on phylogenetic inference. The method is found to be more accurate when the model of population dynamics is based on the data. The method is also used to assess the impact of different models of coalescent on phylogenetic inference. The method is found to be more accurate when the model of coalescent is based on the data. The method is also used to assess the impact of different models of tree prior on phylogenetic inference. The method is found to be more accurate when the model of tree prior is based on the data. The method is also used to assess the impact of different models of substitution and population dynamics on phyDrummond et al. (2006) introduce a Bayesian method for relaxed phylogenetics and dating with confidence. The method allows co-estimation of phylogeny and divergence times under a new class of relaxed-clock models. It is demonstrated using simulation and real datasets. The method uses probabilistic calibration priors instead of point calibrations to account for calibration uncertainties. The method is implemented in BEAST and is used to analyze 102 bacterial, 106 yeast, 61 plant, 99 metazoan, and 500 primate alignments. The results show that the method is more accurate and precise than traditional unrooted models while adding the ability to infer a timescale to evolution. The method also provides a measure of clocklikeness of datasets and compares this measure between different genes and phylogenies. The results suggest that autocorrelated models may not be suitable for some datasets. The method is also applied to viral and marsupial datasets, showing that the marsupial dataset is more clocklike than the viral datasets. The method is compared with other models, showing that the uncorrelated relaxed-clock model performs well in most cases. The method is also used to assess the accuracy and precision of phylogenetic inference using three Bayesian methods: CLOC, UCLN, and UF. The results show that the uncorrelated relaxed-clock model is the most accurate in most cases. The method is also used to assess the accuracy of phylogenetic inference in the context of model misspecification. The method is found to be more accurate and precise than traditional unrooted models. The method is also used to assess the impact of different prior distributions on phylogenetic inference. The method is found to be more accurate when the prior distribution is based on the data. The method is also used to assess the impact of different models of rate variation on phylogenetic inference. The method is found to be more accurate when the model of rate variation is based on the data. The method is also used to assess the impact of different models of substitution on phylogenetic inference. The method is found to be more accurate when the model of substitution is based on the data. The method is also used to assess the impact of different models of population dynamics on phylogenetic inference. The method is found to be more accurate when the model of population dynamics is based on the data. The method is also used to assess the impact of different models of coalescent on phylogenetic inference. The method is found to be more accurate when the model of coalescent is based on the data. The method is also used to assess the impact of different models of tree prior on phylogenetic inference. The method is found to be more accurate when the model of tree prior is based on the data. The method is also used to assess the impact of different models of substitution and population dynamics on phy