UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population

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

[img] Text
BAYSM_countdatapaper.pdf - Accepted version
Access restricted to UCL open access staff until 23 November 2020.

Download (1MB)

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
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 > 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
Downloads since deposit
2Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item