When we have no prior knowledge about the parameter, we may want to specify a generic prior distribution that is flexible enough to be updated by the data. PrioriTree provides multiple such distributions to serve as the candidate choice, where the default choice of each prior has been identified to perform well in many empirical analyses (Gao et al. 2022). PrioriTree will display the distribution of the selected prior, with the associated prior mean and \(95\%\) credible interval listed alongside.
Alternatively, we may have some intuitions (e.g., knowledge learned from previous analyses) about the parameters that we want to express in our priors. PrioriTree allows users to specify these biologically motivated priors in an interactive manner; it provides the flexibility to specify a range of (hyper)priors and dynamically renders the resulting prior distribution according to the specification in real time.
You can also configure other settings (e.g., inferring number of dispersal events between each pair of geographic areas) of the BEAST analysis in PrioriTree as well. According to the input, the changes to the BEAST XML script and methods description are viewable on the fly. At the end, PrioriTree generates a readily runnable BEAST XML script (as well as the associated methods template) to perform the analysis that you conceive.
Below we first provide an in-depth introduction to discrete-geographic phylodynamic inferences, including the discrete-geographic model, dispersal-history inference, phylogenetic-uncertainty accommodation, the prior-sensitivity nature of biogeographic inference, the prior choices provided by PrioriTree, and the MCMC algorithm. We then explain the theory underlying the prior-sensitivity analyses and model-fit-assessment analyses.