Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity
We introduce a dependent Bayesian nonparametric model for the probabilistic modeling of membership of subgroups in a community based on partially replicated data. The focus here is on species-by-site data, i.e. community data where observations at different sites are classified in distinct species....
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ftdatacite:10.48550/arxiv.1402.3093 2023-05-15T13:57:59+02:00 Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity Arbel, Julyan Mengersen, Kerrie Rousseau, Judith 2014 https://dx.doi.org/10.48550/arxiv.1402.3093 https://arxiv.org/abs/1402.3093 unknown arXiv https://dx.doi.org/10.1214/16-aoas944 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Statistics Theory math.ST Methodology stat.ME FOS Mathematics FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2014 ftdatacite https://doi.org/10.48550/arxiv.1402.3093 https://doi.org/10.1214/16-aoas944 2022-04-01T13:06:23Z We introduce a dependent Bayesian nonparametric model for the probabilistic modeling of membership of subgroups in a community based on partially replicated data. The focus here is on species-by-site data, i.e. community data where observations at different sites are classified in distinct species. Our aim is to study the impact of additional covariates, for instance environmental variables, on the data structure, and in particular on the community diversity. To that purpose, we introduce dependence a priori across the covariates, and show that it improves posterior inference. We use a dependent version of the Griffiths-Engen-McCloskey distribution defined via the stick-breaking construction. This distribution is obtained by transforming a Gaussian process whose covariance function controls the desired dependence. The resulting posterior distribution is sampled by Markov chain Monte Carlo. We illustrate the application of our model to a soil microbial dataset acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. This method allows for inference on a number of quantities of interest in ecotoxicology, such as diversity or effective concentrations, and is broadly applicable to the general problem of communities response to environmental variables. : Main Paper: 22 pages, 6 figures. Supplementary Material: 11 pages, 1 figure Text Antarc* Antarctica DataCite Metadata Store (German National Library of Science and Technology) |
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Statistics Theory math.ST Methodology stat.ME FOS Mathematics FOS Computer and information sciences |
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Statistics Theory math.ST Methodology stat.ME FOS Mathematics FOS Computer and information sciences Arbel, Julyan Mengersen, Kerrie Rousseau, Judith Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
topic_facet |
Statistics Theory math.ST Methodology stat.ME FOS Mathematics FOS Computer and information sciences |
description |
We introduce a dependent Bayesian nonparametric model for the probabilistic modeling of membership of subgroups in a community based on partially replicated data. The focus here is on species-by-site data, i.e. community data where observations at different sites are classified in distinct species. Our aim is to study the impact of additional covariates, for instance environmental variables, on the data structure, and in particular on the community diversity. To that purpose, we introduce dependence a priori across the covariates, and show that it improves posterior inference. We use a dependent version of the Griffiths-Engen-McCloskey distribution defined via the stick-breaking construction. This distribution is obtained by transforming a Gaussian process whose covariance function controls the desired dependence. The resulting posterior distribution is sampled by Markov chain Monte Carlo. We illustrate the application of our model to a soil microbial dataset acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. This method allows for inference on a number of quantities of interest in ecotoxicology, such as diversity or effective concentrations, and is broadly applicable to the general problem of communities response to environmental variables. : Main Paper: 22 pages, 6 figures. Supplementary Material: 11 pages, 1 figure |
format |
Text |
author |
Arbel, Julyan Mengersen, Kerrie Rousseau, Judith |
author_facet |
Arbel, Julyan Mengersen, Kerrie Rousseau, Judith |
author_sort |
Arbel, Julyan |
title |
Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
title_short |
Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
title_full |
Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
title_fullStr |
Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
title_full_unstemmed |
Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
title_sort |
bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity |
publisher |
arXiv |
publishDate |
2014 |
url |
https://dx.doi.org/10.48550/arxiv.1402.3093 https://arxiv.org/abs/1402.3093 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://dx.doi.org/10.1214/16-aoas944 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1402.3093 https://doi.org/10.1214/16-aoas944 |
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1766265935243509760 |