This study explores how Bayesian branch-length estimates can be improved by using informed priors.
The authors of this study explore the use of outside information to set informed branch-length priors and compare inferences from these informed analyses to those using default settings. For both the commonly used exponential and the newly proposed compound Dirichlet prior distributions, the incorporation of relevant outside information improves inferences for data sets that have produced problematic branch- and tree-length estimates under default settings. The authors suggest that informed priors are worthy of further exploration for phylogenetics. Prior distributions can have a strong effect on the results of Bayesian analyses. However, no general consensus exists for how priors should be set in all circumstances. Branch-length priors are of particular interest for phylogenetics, because they affect many parameters and biologically relevant inferences have been shown to be sensitive to the chosen prior distribution. (Published Abstract Provided)
Downloads
Similar Publications
- Characterizing the Frequency of Heteroplasmy in Mitochondrial DNA of Tissues Using Next-Generation Sequencing
- Using Clonal Massively Parallel Sequencing to Characterize Heteroplasmy in the mtDNA of Human Head Hair, Pubic Hair, and Buccal Samples
- Decriminalizing or reassembling schools? Implications of removing police from schools for racial and ethnic disparities in criminal justice system contact