Efficient Bayesian analysis of spatial occupancy models
| dc.contributor.advisor | Clark, Allan | |
| dc.contributor.author | Bleki, Zolisa | |
| dc.date.accessioned | 2020-12-30T10:18:00Z | |
| dc.date.available | 2020-12-30T10:18:00Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Species conservation initiatives play an important role in ecological studies. Occupancy models have been a useful tool for ecologists to make inference about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environmental variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the single-season spatial occupancy model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial random effect in our sampler. We also develop OccuSpytial, a statistical package implementing our Gibbs sampler in the Python programming language. The aim of this study is to highlight the computational efficiency that can be obtained by employing several techniques, which include exploiting the sparsity of the precision matrix of the ICAR model and also making use of Polya-Gamma latent variables to obtain closed form expressions for the posterior conditional distributions of the parameters of interest. An algorithm for efficiently sampling from the posterior conditional distribution of the spatial random effects parameter is also developed and presented. To illustrate the sampler's performance a number of simulation experiments are considered, and the results are compared to those obtained by using a Gibbs sampler incorporating Restricted Spatial Regression (RSR) to specify the spatial random effect. Furthermore, we fit our model to the Helmeted guineafowl (Numida meleagris) dataset obtained from the 2nd South African Bird Atlas Project database in order to obtain a distribution map of the species. We compare our results with those obtained from the RSR variant of our sampler, those obtained by using the stocc statistical package (written using the R programming language), and those obtained from not specifying any spatial information about the sites in the data. It was found that using RSR to specify spatial random effects is both statistically and computationally more efficient that specifying them using ICAR. The OccuSpytial implementations of both ICAR and RSR Gibbs samplers has significantly less runtime compared to other implementations it was compared to. | |
| dc.identifier.apacitation | Bleki, Z. (2020). <i>Efficient Bayesian analysis of spatial occupancy models</i>. (Master Thesis). University of Cape Town. Retrieved from http://hdl.handle.net/11427/32469 | en_ZA |
| dc.identifier.chicagocitation | Bleki, Zolisa. <i>"Efficient Bayesian analysis of spatial occupancy models."</i> Master Thesis., University of Cape Town, 2020. http://hdl.handle.net/11427/32469 | en_ZA |
| dc.identifier.citation | Bleki, Z. 2020. Efficient Bayesian analysis of spatial occupancy models. Master Thesis. University of Cape Town. http://hdl.handle.net/11427/32469 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Bleki, Zolisa AB - Species conservation initiatives play an important role in ecological studies. Occupancy models have been a useful tool for ecologists to make inference about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environmental variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the single-season spatial occupancy model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial random effect in our sampler. We also develop OccuSpytial, a statistical package implementing our Gibbs sampler in the Python programming language. The aim of this study is to highlight the computational efficiency that can be obtained by employing several techniques, which include exploiting the sparsity of the precision matrix of the ICAR model and also making use of Polya-Gamma latent variables to obtain closed form expressions for the posterior conditional distributions of the parameters of interest. An algorithm for efficiently sampling from the posterior conditional distribution of the spatial random effects parameter is also developed and presented. To illustrate the sampler's performance a number of simulation experiments are considered, and the results are compared to those obtained by using a Gibbs sampler incorporating Restricted Spatial Regression (RSR) to specify the spatial random effect. Furthermore, we fit our model to the Helmeted guineafowl (Numida meleagris) dataset obtained from the 2nd South African Bird Atlas Project database in order to obtain a distribution map of the species. We compare our results with those obtained from the RSR variant of our sampler, those obtained by using the stocc statistical package (written using the R programming language), and those obtained from not specifying any spatial information about the sites in the data. It was found that using RSR to specify spatial random effects is both statistically and computationally more efficient that specifying them using ICAR. The OccuSpytial implementations of both ICAR and RSR Gibbs samplers has significantly less runtime compared to other implementations it was compared to. DA - 2020 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PY - 2020 T1 - Efficient Bayesian analysis of spatial occupancy models TI - Efficient Bayesian analysis of spatial occupancy models UR - http://hdl.handle.net/11427/32469 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/32469 | |
| dc.identifier.vancouvercitation | Bleki Z. Efficient Bayesian analysis of spatial occupancy models. [Master Thesis]. University of Cape Town, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/32469 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher | University of Cape Town | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject.other | detection probability | |
| dc.subject.other | Markov Chain Monte Carlo | |
| dc.subject.other | Occupancy Modelling | |
| dc.subject.other | Spatial Modelling | |
| dc.subject.other | Species Occurrence | |
| dc.title | Efficient Bayesian analysis of spatial occupancy models | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc | |
| uct.type.publication | Research | |
| uct.type.resource | Master Thesis |
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