Optimal Bayesian experimental designs for complex models
Dehideniya, Mahasen Bandara (2019) Optimal Bayesian experimental designs for complex models. PhD by Publication, Queensland University of Technology.
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Mahasen Bandara Dehideniya Thesis
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Description
The complexity of statistical models that are used to describe biological processes poses significant computational challenges in design of experiments. To address such challenges, in this thesis, new methods are developed in optimisation and approximate inference, and are applied in real-world experiments. The proposed methods enable practitioners to gain greater insight and understanding into the biological processes they are studying, and this is demonstrated by designing experiments to understand important biological processes in epidemiology and ecology such as the spread of infectious diseases and interactions between predator and prey in environmental systems.
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| ID Code: | 131625 |
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| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | McGree, James M. & Drovandi, Christopher C. |
| Keywords: | Approximate Bayesian computation, Synthetic likelihood, Prey and predator model, Foot and mouth disease, Total entropy, Mutual information, Kullback-Leibler divergence |
| DOI: | 10.5204/thesis.eprints.131625 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 22 Aug 2019 15:49 |
| Last Modified: | 16 Jan 2025 00:54 |
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