Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"log-Gaussian Cox process"”.
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Fast methods for fitting log-Gaussian Cox process models in ecology.
Log-Gaussian Cox processes (LGCPs) offer a framework for regression-style modelling of point patterns that can accommodate latent effects. These latent effects can be used to account for missing predictors or other sources of clustering that could not be explained by a Poisson process. Such models …
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Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology
… hierarchical models specified using a latent Gaussian Markov Random Field (GMRF) are evaluated for use in analyzing large complex spatial and spatio-temporal data with the goal of contributing to an interdisciplinary effort of developing an eco-epidemiological model that quantifies the …
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A Bayesian approach to discovery of latent dependency in point-referenced data
… The model highlights how to incorporate the ecological perspective into the hierarchical modeling structure, motivating the need of considering the underlying ecological structure to better understand the driving dynamics in the animal behaviors. Then, a statistical model is proposed to explain …
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Computational Bayesian inference using low discrepancy sequences
… partial differential equation approach to a Log-Gaussian Cox Process, and use an LDS to approximate the latent parameters of the model. Our results show that for a fixed number of points or computational time, LDS methods can outperform general grid-based methods, leading to better marginal …
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Near-Optimal Sensor Placement for Detection of Poisson Distributed Targets
… We model target arrivals using a Poisson process to capture the inherent randomness of event occurrences and emphasize the importance of accounting for uncertainty in the sensor placement strategy. To tackle this, we propose a computationally efficient approximation method based on a lower …
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Heterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modeling
… for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining …
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Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data
… the transformation of most disciplines in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. This dissertation focuses on modeling ``omics'' data in various NGS applications with a primary goal of developing novel …
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Modelling of survival and incidence for colorectal cancer in Malaysia
… colorectal cancer patients in Malaysia with histologically verified primary colorectal cancer who were diagnosed between 2008 and 2013 (ICD-10, C18-C20), recorded in the database of National Cancer Patient Registry- Colorectal Cancer (NCPR-CC) Malaysia. We investigated the effect of individual …