A variational Bayes approach to the analysis of occupancy models

 

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dc.contributor.author Clark, Allan E en_ZA
dc.contributor.author Altwegg, Res en_ZA
dc.contributor.author Ormerod, John T en_ZA
dc.date.accessioned 2016-03-08T10:55:11Z
dc.date.available 2016-03-08T10:55:11Z
dc.date.issued 2016 en_ZA
dc.identifier.citation Clark, A. E., Altwegg, R., & Ormerod, J. T. (2016). A variational Bayes approach to the analysis of occupancy models. PloS one, 11(2), e0148966. doi:10.1371/journal.pone.0148966 en_ZA
dc.identifier.uri http://dx.doi.org/10.1371/journal.pone.0148966 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/17574
dc.description.abstract Detection-nondetection data are often used to investigate species range dynamics using Bayesian occupancy models which rely on the use of Markov chain Monte Carlo (MCMC) methods to sample from the posterior distribution of the parameters of the model. In this article we develop two Variational Bayes (VB) approximations to the posterior distribution of the parameters of a single-season site occupancy model which uses logistic link functions to model the probability of species occurrence at sites and of species detection probabilities. This task is accomplished through the development of iterative algorithms that do not use MCMC methods. Simulations and small practical examples demonstrate the effectiveness of the proposed technique. We specifically show that (under certain circumstances) the variational distributions can provide accurate approximations to the true posterior distributions of the parameters of the model when the number of visits per site ( K ) are as low as three and that the accuracy of the approximations improves as K increases. We also show that the methodology can be used to obtain the posterior distribution of the predictive distribution of the proportion of sites occupied (PAO). en_ZA
dc.language.iso eng en_ZA
dc.publisher Public Library of Science en_ZA
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. en_ZA
dc.rights.uri http://creativecommons.org/licenses/by/4.0 en_ZA
dc.source PLoS One en_ZA
dc.source.uri http://journals.plos.org/plosone en_ZA
dc.subject.other Tangents en_ZA
dc.subject.other Statistical distributions en_ZA
dc.subject.other Approximation methods en_ZA
dc.subject.other Simulation and modeling en_ZA
dc.subject.other Bayesian method en_ZA
dc.subject.other Birds en_ZA
dc.subject.other Probability distribution en_ZA
dc.subject.other Algorithms en_ZA
dc.title A variational Bayes approach to the analysis of occupancy models en_ZA
dc.type Journal Article en_ZA
dc.rights.holder © 2016 Clark et al en_ZA
uct.type.publication Research en_ZA
uct.type.resource Article en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Statistical Sciences en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Clark, A. E., Altwegg, R., & Ormerod, J. T. (2016). A variational Bayes approach to the analysis of occupancy models. <i>PLoS One</i>, http://hdl.handle.net/11427/17574 en_ZA
dc.identifier.chicagocitation Clark, Allan E, Res Altwegg, and John T Ormerod "A variational Bayes approach to the analysis of occupancy models." <i>PLoS One</i> (2016) http://hdl.handle.net/11427/17574 en_ZA
dc.identifier.vancouvercitation Clark AE, Altwegg R, Ormerod JT. A variational Bayes approach to the analysis of occupancy models. PLoS One. 2016; http://hdl.handle.net/11427/17574. en_ZA
dc.identifier.ris TY - Journal Article AU - Clark, Allan E AU - Altwegg, Res AU - Ormerod, John T AB - Detection-nondetection data are often used to investigate species range dynamics using Bayesian occupancy models which rely on the use of Markov chain Monte Carlo (MCMC) methods to sample from the posterior distribution of the parameters of the model. In this article we develop two Variational Bayes (VB) approximations to the posterior distribution of the parameters of a single-season site occupancy model which uses logistic link functions to model the probability of species occurrence at sites and of species detection probabilities. This task is accomplished through the development of iterative algorithms that do not use MCMC methods. Simulations and small practical examples demonstrate the effectiveness of the proposed technique. We specifically show that (under certain circumstances) the variational distributions can provide accurate approximations to the true posterior distributions of the parameters of the model when the number of visits per site ( K ) are as low as three and that the accuracy of the approximations improves as K increases. We also show that the methodology can be used to obtain the posterior distribution of the predictive distribution of the proportion of sites occupied (PAO). DA - 2016 DB - OpenUCT DO - 10.1371/journal.pone.0148966 DP - University of Cape Town J1 - PLoS One LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - A variational Bayes approach to the analysis of occupancy models TI - A variational Bayes approach to the analysis of occupancy models UR - http://hdl.handle.net/11427/17574 ER - en_ZA


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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.