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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Spatiotemporal Modeling With Gaussian Processes, Qing He
Bayesian Spatiotemporal Modeling With Gaussian Processes, Qing He
Electronic Theses and Dissertations, 2020-2023
Bayesian spatiotemporal models have been successfully applied to various fields of science, such as ecology and epidemiology. The complicated nature of spatiotemporal patterns can be well represented through priors such as Gaussian processes. This dissertation is focused on two applications of Bayesian spatiotemporal models: a) anomaly detection for spatiotemporal data with missingness and b) zero-inflated spatiotemporal count data analysis. Missingness in spatiotemporal data prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of most data. This project is motivated by a challenge provided by the National Science Foundation (NSF) and the National Geospatial-Intelligence Agency (NGA). …
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Electronic Theses and Dissertations, 2020-2023
Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation …
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Electronic Theses and Dissertations, 2020-2023
Sequential multiple change point detection concerns the identification of multiple points in time where the systematic behavior of a statistical process changes. A special case of this problem, called online anomaly detection, occurs when the goal is to detect the first change and then signal an alert to an analyst for further investigation. This dissertation concerns the use of methods based on kernel functions and support vectors to detect changes. A variety of support vector-based methods are considered, but the primary focus concerns Least Squares Support Vector Data Description (LS-SVDD). LS-SVDD constructs a hypersphere in a kernel space to bound …