P3: Phylogenetic Posterior Prediction in RevBayes
Author(s) -
Sebastian Höhna,
Lyndon M. Coghill,
Genevieve G. Mount,
Robert C. Thomson,
Jeremy M. Brown
Publication year - 2017
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msx286
Subject(s) - inference , posterior probability , bayesian inference , biology , bayesian probability , software , phylogenetic tree , predictive inference , computer science , machine learning , data mining , statistics , artificial intelligence , frequentist inference , mathematics , genetics , gene , programming language
Tests of absolute model fit are crucial in model-based inference because poorly structured models can lead to biased parameter estimates. In Bayesian inference, posterior predictive simulations can be used to test absolute model fit. However, such tests have not been commonly practiced in phylogenetic inference due to a lack of convenient and flexible software. Here, we describe our newly implemented tests of model fit using posterior predictive testing, based on both data- and inference-based test statistics, in the phylogenetics software RevBayes. This new implementation makes a large spectrum of models available for use through a user-friendly and flexible interface.
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