Skip to main content
Kent Academic Repository

Bayesian Nonparametric Models for Modelling Ecological Data and Stochastic Processes for Modelling Species Interactions

Diana, Alex (2020) Bayesian Nonparametric Models for Modelling Ecological Data and Stochastic Processes for Modelling Species Interactions. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:82428)

Language: English

Download this file
[thumbnail of 300thesis.pdf]


In this thesis, we present four manuscripts, described in the second to fifth chapter. Chapter 2 presents a Bayesian nonparametric model for capture-recapture (CR) data collected at different sites and for several years. To estimate arrival and departure patterns at the different sites and years, we build an extension of the Dirichlet process, the Hierarchical Dependent Dirichlet process, which allows us to perform density estimation jointly across different sites and in the presence of covariates. In this case, we use a year-specific covariate, and model the correlation structure of the covariate across years using a multivariate Gaussian process. In Chapter 3, we present a model for estimating entry and exit patterns, as well as the population size, using count data (CD), by employing a Polya Tree (PT) prior. In Chapter 4 we present several extensions of chapter 3. More specifically, we extend the model to CR and to ring-recovery data and develop a joint model for CR and CD. In addition, we consider the case when multiple data-sets are modelled at the same time, by defining a hierarchical extension of the PT, which we define as Hierarchical Logistic PT. Finally, we extend the model to the case of long time series, by borrowing ideas from the Optional PT. Chapter 5 presents a spatial model to estimate interactions between multiple species using CR data. The model uses a vector of interaction point process (IPP), which allows us to estimate interactions between and within species. The use of an IPP leads to an intractable ratio of normalising constants (RNC), and hence we use the Monte Carlo Metropolis Hastings algorithm to approximate the RNC with an importance sampling estimate. The supplementary material for each paper is presented in the appendix.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Matechou, Eleni
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 17 Aug 2020 09:24 UTC
Last Modified: 12 Dec 2022 22:07 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Diana, Alex.

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.