%0 Journal Article %1 jones_evolutionary_2010 %A Jones, Nick S. %A Moriarty, John %D 2010 %J arXiv:1004.4668 %K - Analysis, Biology Data Learning Learning, Machine Methods, Physics Probability, Quantitative Statistics and %R 10.1098/rsif.2012.0616 %T Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies %U http://arxiv.org/abs/1004.4668 %X 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 due 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 nonparametric 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.

@article{jones_evolutionary_2010, abstract = {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 due 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 nonparametric 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.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Jones, Nick S. and Moriarty, John}, biburl = {https://www.bibsonomy.org/bibtex/2a975131078bd273494e09a646642a2a9/yourwelcome}, doi = {10.1098/rsif.2012.0616}, interhash = {6c5e31b8bde73f7089568c9d3f09a0d7}, intrahash = {a975131078bd273494e09a646642a2a9}, journal = {arXiv:1004.4668}, keywords = {- Analysis, Biology Data Learning Learning, Machine Methods, Physics Probability, Quantitative Statistics and}, month = apr, note = {Journal of the Royal Society Interface vol. 10 no. 78 20120616 (2013)}, shorttitle = {Evolutionary {Inference} for {Function}-valued {Traits}}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Evolutionary inference for function-valued traits: {Gaussian} process regression on phylogenies}, url = {http://arxiv.org/abs/1004.4668}, urldate = {2013-01-18}, year = 2010 }