Estimating Bayesian Phylogenetic Information Content

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...

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Bibliographic Details
Published in:Systematic biology 2016-11, Vol.65 (6), p.1009-1023
Main Authors: Lewis, Paul O., Chen, Ming-Hui, Kuo, Lynn, Lewis, Louise A., Fučíková, Karolina, Neupane, Suman, Wang, Yu-Bo, Shi, Daoyuan
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Classification - methods
Conditional probabilities
Data files
Datasets
Decision trees
Entropy
Estimating techniques
Information content
Models, Genetic
Phylogenetics
Phylogeny
Regular
Systematics
Taxa
Topology
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Summary: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.
ISSN:1063-5157
1076-836X
DOI:10.1093/sysbio/syw042