WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods
The computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided.
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dig-cirad-fr-5722172024-01-28T21:51:30Z http://agritrop.cirad.fr/572217/ http://agritrop.cirad.fr/572217/ WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods. Gimenez Olivier, Bonner Simon J., King Ruth, Parker Richard A., Brooks Stephen P., Jamieson L.E., Grosbois Vladimir, Morgan Byron J., Thomas Len. 2009. In : Modeling demographic processes in marked populations. Thomson David L. (ed.), Cooch Evan G. (ed.), Conroy Michael J. (ed.). New York : Springer [Etats-Unis], Résumé, 883-915. (Environmental and ecological statistics series, 3) ISBN 978-0-387-78150-1https://doi.org/10.1007/978-0-387-78151-8_41 <https://doi.org/10.1007/978-0-387-78151-8_41> WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods Gimenez, Olivier Bonner, Simon J. King, Ruth Parker, Richard A. Brooks, Stephen P. Jamieson, L.E. Grosbois, Vladimir Morgan, Byron J. Thomas, Len eng 2009 Springer [Etats-Unis] Modeling demographic processes in marked populations U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche L20 - Écologie animale écologie animale étude de cas animal sauvage capture animale canard dynamique des populations modèle mathématique méthode statistique densité survie http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24103 http://aims.fao.org/aos/agrovoc/c_24105 http://aims.fao.org/aos/agrovoc/c_2406 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_2186 http://aims.fao.org/aos/agrovoc/c_7538 Amérique du Nord http://aims.fao.org/aos/agrovoc/c_5219 The computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided. book_section info:eu-repo/semantics/bookPart Chapter info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/572217/1/document_572217.pdf application/pdf Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-0-387-78151-8_41 10.1007/978-0-387-78151-8_41 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=203754 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-0-387-78151-8_41 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-0-387-78151-8_41 |
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U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche L20 - Écologie animale écologie animale étude de cas animal sauvage capture animale canard dynamique des populations modèle mathématique méthode statistique densité survie http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24103 http://aims.fao.org/aos/agrovoc/c_24105 http://aims.fao.org/aos/agrovoc/c_2406 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_2186 http://aims.fao.org/aos/agrovoc/c_7538 http://aims.fao.org/aos/agrovoc/c_5219 U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche L20 - Écologie animale écologie animale étude de cas animal sauvage capture animale canard dynamique des populations modèle mathématique méthode statistique densité survie http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24103 http://aims.fao.org/aos/agrovoc/c_24105 http://aims.fao.org/aos/agrovoc/c_2406 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_2186 http://aims.fao.org/aos/agrovoc/c_7538 http://aims.fao.org/aos/agrovoc/c_5219 |
spellingShingle |
U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche L20 - Écologie animale écologie animale étude de cas animal sauvage capture animale canard dynamique des populations modèle mathématique méthode statistique densité survie http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24103 http://aims.fao.org/aos/agrovoc/c_24105 http://aims.fao.org/aos/agrovoc/c_2406 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_2186 http://aims.fao.org/aos/agrovoc/c_7538 http://aims.fao.org/aos/agrovoc/c_5219 U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche L20 - Écologie animale écologie animale étude de cas animal sauvage capture animale canard dynamique des populations modèle mathématique méthode statistique densité survie http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24103 http://aims.fao.org/aos/agrovoc/c_24105 http://aims.fao.org/aos/agrovoc/c_2406 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_2186 http://aims.fao.org/aos/agrovoc/c_7538 http://aims.fao.org/aos/agrovoc/c_5219 Gimenez, Olivier Bonner, Simon J. King, Ruth Parker, Richard A. Brooks, Stephen P. Jamieson, L.E. Grosbois, Vladimir Morgan, Byron J. Thomas, Len WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods |
description |
The computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided. |
format |
book_section |
topic_facet |
U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche L20 - Écologie animale écologie animale étude de cas animal sauvage capture animale canard dynamique des populations modèle mathématique méthode statistique densité survie http://aims.fao.org/aos/agrovoc/c_427 http://aims.fao.org/aos/agrovoc/c_24392 http://aims.fao.org/aos/agrovoc/c_24103 http://aims.fao.org/aos/agrovoc/c_24105 http://aims.fao.org/aos/agrovoc/c_2406 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_2186 http://aims.fao.org/aos/agrovoc/c_7538 http://aims.fao.org/aos/agrovoc/c_5219 |
author |
Gimenez, Olivier Bonner, Simon J. King, Ruth Parker, Richard A. Brooks, Stephen P. Jamieson, L.E. Grosbois, Vladimir Morgan, Byron J. Thomas, Len |
author_facet |
Gimenez, Olivier Bonner, Simon J. King, Ruth Parker, Richard A. Brooks, Stephen P. Jamieson, L.E. Grosbois, Vladimir Morgan, Byron J. Thomas, Len |
author_sort |
Gimenez, Olivier |
title |
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods |
title_short |
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods |
title_full |
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods |
title_fullStr |
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods |
title_full_unstemmed |
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods |
title_sort |
winbugs for population ecologists: bayesian modeling using markov chain monte carlo methods |
publisher |
Springer [Etats-Unis] |
url |
http://agritrop.cirad.fr/572217/ http://agritrop.cirad.fr/572217/1/document_572217.pdf |
work_keys_str_mv |
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_version_ |
1792498586434404352 |