A particular requirement of Bayesian phylogenetic inference is the responsibility given to users to specify a prior probability distribution on the shape of the phylogeny (node ages and branching order). This can be either a benefit or a burden, largely depending on whether an obvious prior distribution presents itself for the data at hand. For example, the coalescent prior [
56 ,
57 ] is a commonly used prior for population-level data and has been extended to include various forms of demographic functions [
58 (link),
59 (link)], sub-divided populations [
60 (link)], and other complexities. Traditional speciation models such as the Yule process [
61 ] and various birth–death models [
62 (link),
63 (link)] can also provide useful priors for species-level data. Such models generally have a number of hyperparameters (for example, effective population size, growth rate, or speciation and extinction rates), which, under a Bayesian framework, can be sampled to provide a posterior distribution of these potentially interesting biological quantities.
In some cases, the choice of prior on the phylogenetic tree can exert a strong influence on inferences made from a given dataset [
64 (link)]. The sensitivity of inference results to the prior chosen will be largely dependent on the data analyzed and few general recommendations can be made. It is, however, good practice to perform the MCMC analysis without any data in order to sample wholly from the prior distribution. This distribution can be compared to the posterior distribution for parameters of interest in order to examine the relative influence of the data and the prior (
Figure 3 ).
56 ,
57 ] is a commonly used prior for population-level data and has been extended to include various forms of demographic functions [
58 (link),
59 (link)], sub-divided populations [
60 (link)], and other complexities. Traditional speciation models such as the Yule process [
61 ] and various birth–death models [
62 (link),
63 (link)] can also provide useful priors for species-level data. Such models generally have a number of hyperparameters (for example, effective population size, growth rate, or speciation and extinction rates), which, under a Bayesian framework, can be sampled to provide a posterior distribution of these potentially interesting biological quantities.
In some cases, the choice of prior on the phylogenetic tree can exert a strong influence on inferences made from a given dataset [
64 (link)]. The sensitivity of inference results to the prior chosen will be largely dependent on the data analyzed and few general recommendations can be made. It is, however, good practice to perform the MCMC analysis without any data in order to sample wholly from the prior distribution. This distribution can be compared to the posterior distribution for parameters of interest in order to examine the relative influence of the data and the prior (
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