![]() ![]() ![]() ![]() Although the first of these formulations has some attractive properties, the algorithm we present for computing its prior density is computationally intensive. We describe two formulations, one in which the calibration information informs the prior on ranked tree topologies, through the (conditional) prior, and the other which factorizes the prior on divergence times and ranked topologies, thus allowing uniform, or any arbitrary prior distribution on ranked topologies. All tree priors in this class separate ancestral node heights into a set of "calibrated nodes" and "uncalibrated nodes" such that the marginal distribution of the calibrated nodes is user-specified whereas the density ratio of the birth-death prior is retained for trees with equal values for the calibrated nodes. Here we introduce a general class of multiple calibration birth-death tree priors for use in Bayesian phylogenetic inference. Calibrated birth-death phylogenetic time-tree priors for bayesian inference. ![]()
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