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, that is, community data where observations at different sites are classified in distinct speci...
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ftunivdauphine:oai:basepub.dauphine.psl.eu:123456789/12764 2023-05-15T13:42:29+02:00 Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity Arbel, Julyan Mengersen, Kerrie Rousseau, Judith Queensland University of Technology;Australie Collegio Carlo Alberto;Italie 2017-01-16T11:16:08Z https://basepub.dauphine.fr/handle/123456789/12764 https://arxiv.org/abs/1402.3093v2 en eng Paris The Annals of Applied Statistics 10 3 2016 1496-1516 10.1214/16-AOAS944 The Institute of Mathematical Statistics oui 1932-6157 https://basepub.dauphine.fr/handle/123456789/12764 https://arxiv.org/abs/1402.3093v2 Stick-breaking representation Griffiths-Engen-McCloskey distribution Gaussian process Covariate-dependent model Bayesian nonparametrics 519 Probabilités et mathématiques appliquées Article accepté pour publication ou publié 2017 ftunivdauphine 2022-05-01T15:50:10Z 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, that is, 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 this end, 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 data set 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 community response to environmental variables. non non oui recherche International Article in Journal/Newspaper Antarc* Antarctica Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) |
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Open Polar |
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Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) |
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ftunivdauphine |
language |
English |
topic |
Stick-breaking representation Griffiths-Engen-McCloskey distribution Gaussian process Covariate-dependent model Bayesian nonparametrics 519 Probabilités et mathématiques appliquées |
spellingShingle |
Stick-breaking representation Griffiths-Engen-McCloskey distribution Gaussian process Covariate-dependent model Bayesian nonparametrics 519 Probabilités et mathématiques appliquées Arbel, Julyan Mengersen, Kerrie Rousseau, Judith Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity |
topic_facet |
Stick-breaking representation Griffiths-Engen-McCloskey distribution Gaussian process Covariate-dependent model Bayesian nonparametrics 519 Probabilités et mathématiques appliquées |
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, that is, 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 this end, 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 data set 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 community response to environmental variables. non non oui recherche International |
author2 |
Queensland University of Technology;Australie Collegio Carlo Alberto;Italie |
format |
Article in Journal/Newspaper |
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 |
Paris |
publishDate |
2017 |
url |
https://basepub.dauphine.fr/handle/123456789/12764 https://arxiv.org/abs/1402.3093v2 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
The Annals of Applied Statistics 10 3 2016 1496-1516 10.1214/16-AOAS944 The Institute of Mathematical Statistics oui 1932-6157 https://basepub.dauphine.fr/handle/123456789/12764 https://arxiv.org/abs/1402.3093v2 |
_version_ |
1766168397628833792 |