Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics
The stock and recruitment relationship is fundamental to the management of fishery natural resources. However, infering stock-recruitment relationships is a challenging problem because of the limited available data, the collection of plausible models, and the biological characteristics that should b...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.533.3538 2023-05-15T15:27:27+02:00 Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics Ra Fronczyk Athanasios Kottas Stephan Munch The Pennsylvania State University CiteSeerX Archives 2011 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.533.3538 http://ams.soe.ucsc.edu/share/technical-reports/2009/ucsc-soe-09-17.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.533.3538 http://ams.soe.ucsc.edu/share/technical-reports/2009/ucsc-soe-09-17.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://ams.soe.ucsc.edu/share/technical-reports/2009/ucsc-soe-09-17.pdf KEY WORDS Dirichlet process Markov chain Monte Carlo Multivariate normal mixtures North Atlantic cod Stock biomass. 1 text 2011 ftciteseerx 2016-01-08T10:42:37Z The stock and recruitment relationship is fundamental to the management of fishery natural resources. However, infering stock-recruitment relationships is a challenging problem because of the limited available data, the collection of plausible models, and the biological characteristics that should be reflected in the model. Motivated by limitations of traditional parametric stock-recruitment models, we propose a Bayesian nonparametric approach based on a mixture model for the joint distribution of log-reproductive success and stock biomass. Flexible mixture modeling for this bivariate distribution yields rich inference for the stock-recruitment relationship through the implied conditional distribution of log-reproductive success given stock biomass. The method is illustrated with cod data from six regions of the North Atlantic, including comparison with simpler Bayesian parametric and semiparametric models. Text atlantic cod North Atlantic Unknown |
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KEY WORDS Dirichlet process Markov chain Monte Carlo Multivariate normal mixtures North Atlantic cod Stock biomass. 1 |
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KEY WORDS Dirichlet process Markov chain Monte Carlo Multivariate normal mixtures North Atlantic cod Stock biomass. 1 Ra Fronczyk Athanasios Kottas Stephan Munch Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics |
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KEY WORDS Dirichlet process Markov chain Monte Carlo Multivariate normal mixtures North Atlantic cod Stock biomass. 1 |
description |
The stock and recruitment relationship is fundamental to the management of fishery natural resources. However, infering stock-recruitment relationships is a challenging problem because of the limited available data, the collection of plausible models, and the biological characteristics that should be reflected in the model. Motivated by limitations of traditional parametric stock-recruitment models, we propose a Bayesian nonparametric approach based on a mixture model for the joint distribution of log-reproductive success and stock biomass. Flexible mixture modeling for this bivariate distribution yields rich inference for the stock-recruitment relationship through the implied conditional distribution of log-reproductive success given stock biomass. The method is illustrated with cod data from six regions of the North Atlantic, including comparison with simpler Bayesian parametric and semiparametric models. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Ra Fronczyk Athanasios Kottas Stephan Munch |
author_facet |
Ra Fronczyk Athanasios Kottas Stephan Munch |
author_sort |
Ra Fronczyk |
title |
Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics |
title_short |
Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics |
title_full |
Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics |
title_fullStr |
Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics |
title_full_unstemmed |
Flexible modeling for stockrecruitment relationships using Bayesian nonparametric mixtures,” Environmental and Ecological Statistics |
title_sort |
flexible modeling for stockrecruitment relationships using bayesian nonparametric mixtures,” environmental and ecological statistics |
publishDate |
2011 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.533.3538 http://ams.soe.ucsc.edu/share/technical-reports/2009/ucsc-soe-09-17.pdf |
genre |
atlantic cod North Atlantic |
genre_facet |
atlantic cod North Atlantic |
op_source |
http://ams.soe.ucsc.edu/share/technical-reports/2009/ucsc-soe-09-17.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.533.3538 http://ams.soe.ucsc.edu/share/technical-reports/2009/ucsc-soe-09-17.pdf |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766357890311913472 |