Diana, A;
Griffin, J;
Matechou, E;
(2019)
A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population.
In: Argiento, Raffaele and Durante, Daniele and Wade, Sara, (eds.)
Proceedings of Bayesian Young Statisticians Meeting 2018 - BAYSM2018.
(pp. pp. 3-11).
Springer
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Abstract
Many ecological sampling schemes do not allow for unique marking of individuals. Instead, only counts of individuals detected on each sampling occasion are available. In this paper, we propose a novel approach for modelling count data in an open population where individuals can arrive and depart from the site during the sampling period. A Bayesian nonparametric prior, known as Polya Tree, is used for modelling the bivariate density of arrival and departure times. Thanks to this choice, we can easily incorporate prior information on arrival and departure density while still allowing the model to flexibly adjust the posterior inference according to the observed data. Moreover, the model provides great scalability as the complexity does not depend on the population size but just on the number of sampling occasions, making it particularly suitable for data-sets with high numbers of detections. We apply the new model to count data of newts collected by the Durrell Institute of Conservation and Ecology, University of Kent
| Type: | Proceedings paper |
|---|---|
| Title: | A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population |
| Event: | Bayesian Young Statisticians Meeting 2018 - BAYSM2018 |
| Location: | University of Warwick, Coventry, UK |
| Dates: | 02 July 2018 - 03 July 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-30611-3_1 |
| Publisher version: | https://doi.org/10.1007/978-3-030-30611-3_1 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Bayesian nonparametrics, Polya Tree, Count Data, Statistical Ecology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10067912 |
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