Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies

Nick S. Jones, John Moriarty

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    Biological data objects often have both of the following features: (i) they are functions rather than single numbers or vectors, and (ii) they are correlated owing to phylogenetic relationships. In this paper, we give a flexible statistical model for such data, by combining assumptions from phylogenetics with Gaussian processes. We describe its use as a non-parametric Bayesian prior distribution, both for prediction (placing posterior distributions on ancestral functions) and model selection (comparing rates of evolution across a phylogeny, or identifying the most likely phylogenies consistent with the observed data). Our work is integrative, extending the popular phylogenetic Brownian motion and Ornstein-Uhlenbeck models to functional data and Bayesian inference, and extending Gaussian process regression to phylogenies. We provide a brief illustration of the application of our method. © 2012 The Author(s) Published by the Royal Society. All rights reserved.
    Original languageEnglish
    Article number20120616
    JournalJournal of the Royal Society Interface
    Issue number78
    Publication statusPublished - 6 Jan 2013


    • Gaussian processes
    • Morphometrics
    • Phylogenetic inference


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