Contributions to Bayesian transdimensional algorithms
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Description
When conducting statistical data analyses, often multiple competing mathematical models are proposed. Assigning a weight to each model depending on how well they fit the data is a computationally challenging problem, especially when the mathematical models being considered are complex. This thesis develops novel statistical algorithms for addressing this model selection problem that are more computationally efficient and automated compared to previous approaches. A motivating example in the geosciences is presented, and subsequently, the novel algorithms demonstrated on challenging new examples and canonical examples from existing literature.
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| ID Code: | 244544 | ||
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| Item Type: | QUT Thesis (PhD) | ||
| Supervisor: | Drovandi, Christopher, White, Gentry, & Sutton, Matthew | ||
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| Keywords: | Reversible Jump Markov Chain Monte Carlo, Transports, Annealing, Bayesian, Transdimensional, Inference, Electromagnetic, Induced Polarisation, Sequential Monte Carlo | ||
| DOI: | 10.5204/thesis.eprints.244544 | ||
| Pure ID: | 149669321 | ||
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Mathematical Sciences |
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| Institution: | Queensland University of Technology | ||
| Deposited On: | 17 Nov 2023 03:47 | ||
| Last Modified: | 17 Nov 2023 03:47 |
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