Estimating Bayesian Phylogenetic Information Content.

TitleEstimating Bayesian Phylogenetic Information Content.
Publication TypeJournal Article
Year of Publication2016
AuthorsLewis, Paul O., Ming-Hui Chen, Lynn Kuo, Louise A. Lewis, Karolina Fučíková, Suman Neupane, Yu-Bo Wang, and Daoyuan Shi
JournalSyst Biol
Date Published2016 Nov
KeywordsBayes Theorem, Classification, Models, Genetic, Phylogeny

Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information content. If the data have no information relevant to ranking tree topologies beyond the information supplied by the prior, the posterior and prior will be identical. Information in data discourages consideration of some hypotheses allowed by the prior, resulting in a posterior distribution that is more concentrated (has lower entropy) than the prior. We focus on measuring information about tree topology using marginal posterior distributions of tree topologies. We show that both the accuracy and the computational efficiency of topological information content estimation improve with use of the conditional clade distribution, which also allows topological information content to be partitioned by clade. We explore two important applications of our method: providing a compelling definition of saturation and detecting conflict among data partitions that can negatively affect analyses of concatenated data. [Bayesian; concatenation; conditional clade distribution; entropy; information; phylogenetics; saturation.].

Alternate JournalSyst Biol
Original PublicationEstimating Bayesian phylogenetic information content.
PubMed ID27155008
PubMed Central IDPMC5066063
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States