Bayesian design for sampling anomalous data on river networks
Buchhorn, Katie (2024) Bayesian design for sampling anomalous data on river networks. PhD by Publication, Queensland University of Technology.
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Katie Buchhorn Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
Data is fundamental to good decision making, but data collection is often costly and difficult. Efficient designs for collecting high-quality, relevant data are therefore essential. This research advances Bayesian methods for experimental design and machine learning methods for anomaly detection, applying these advancements to the monitoring of river networks. This helps in understanding ecosystems and promoting sustainable management. The methods handle complex dependencies and high-dimensional data and are widely applicable to other challenges in environment, health, business, and society.
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| ID Code: | 250823 |
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| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | McGree, James, Santos Fernandez, Edgar, Wu, Paul, & Mengersen, Kerrie |
| Keywords: | Bayesian design, Bayesian statistics, Experimental design, Anomaly detection, River network, neural network, Optimal design, Environmental |
| DOI: | 10.5204/thesis.eprints.250823 |
| Divisions: | Current > Research Centres > Centre for Data Science Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 26 Jul 2024 14:29 |
| Last Modified: | 17 Jan 2025 00:52 |
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